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+page_content='IEEE INTERNET OF THINGS JOURNAL  1  Ancilia: Scalable Intelligent Video Surveillance for  the Artificial Intelligence of Things  Armin Danesh Pazho∗,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Student Member,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' IEEE,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Christopher Neff∗,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Student Member,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' IEEE,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ghazal Alinezhad  Noghre,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Student Member,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' IEEE,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Babak Rahimi Ardabili,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Student Member,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' IEEE,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Shanle Yao,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Mohammadreza  Baharani,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Member,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' IEEE,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Hamed Tabkhi,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Member,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' IEEE  Abstract—With the advancement of vision-based artificial in-  telligence,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' the proliferation of the Internet of Things connected  cameras,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' and the increasing societal need for rapid and eq-  uitable security,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' the demand for accurate real-time intelligent  surveillance has never been higher.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This article presents Ancilia,  an end-to-end scalable, intelligent video surveillance system for  the Artificial Intelligence of Things.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia brings state-of-the-  art artificial intelligence to real-world surveillance applications  while respecting ethical concerns and performing high-level  cognitive tasks in real-time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia aims to revolutionize the  surveillance landscape, to bring more effective, intelligent, and  equitable security to the field, resulting in safer and more secure  communities without requiring people to compromise their right  to privacy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Index Terms—Surveillance, artificial intelligence, IoT, com-  puter vision, application, real-world, real-time, edge, anomaly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' INTRODUCTION  There is a growing need for effective and efficient surveil-  lance technologies that can be deployed to protect our cities,  people, and infrastructure.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' For example, in Itaewon, South  Korea, a holiday celebration left over 150 dead due to severe  overcrowding, with many blaming the tragedy on careless  government oversight [1].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' In Moore County, North Carolina,  directed attacks against two power substations left over 45,000  residents without power for days as technicians rushed to  restore power and authorities struggled to find the source of  the attacks [2].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' With enough forewarning through smart video  surveillance, they could have been prevented.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  With the recent emergence of the Artificial Intelligence  of Things (AIoT), some surveillance solution providers have  started adding basic forms of artificial intelligence to their  systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' However, their methods are still naive and unable  to enhance security in a truly meaningful way [3].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This is  because, while a lot of research is conducted on tasks that  would benefit surveillance systems, most works focus on  algorithmic improvements in a lab environment instead of  paying attention to factors that are prevalent in real-world  scenarios [4], [5].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Most research focuses on a single algorithm  and how to tweak it to get the best possible results on readily  available datasets that often do not reflect a real surveillance  environment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Few works explore how different algorithms af-  fect the performance of other downstream algorithms in multi-  The authors are with the Electrical and Computer Engineering Department,  The University of North Carolina at Charlotte, Charlotte, NC, 28223 USA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  {adaneshp, cneff1, galinezh, brahimia, mbaharan, htabkhiv}@uncc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='edu  ∗ Corresponding authors have equal contribution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  algorithm systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Few still explore the effects of noise (both  data derived and the system produced) in end-to-end accuracy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Beyond this, real-world intelligent surveillance necessitates  real-time performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The cognitive abilities of advanced  artificial intelligence are only helpful if they can be provided  to security personnel quickly enough to take appropriate action  before it is too late.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Conceptual overview of Ancilia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  In this article, we present Ancilia, the first end-to-end  scalable, intelligent video surveillance system able to perform  high-level cognitive tasks in real-time while achieving state-  of-the-art results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia takes advantage of the prevalence  of cameras in the Internet of Things (IoT) and uses localized  servers and existing cameras, facilitating processing on the  edge without the need for additional infrastructure upgrades.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 1, Ancilia exists within three logical and  physical segments: the edge, the cloud, and user devices.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  The edge uses a plethora of advanced artificial intelligence  algorithms processing data received from cameras to facilitate  intelligent security.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Using a single workstation to perform edge  processing, Ancilia can monitor up to 4 cameras in real-time at  30 FPS, or up to 8 cameras at 15 FPS, in scenarios with both  medium and heavy crowd density.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia performs high-level  cognitive tasks (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' action recognition, anomaly detection)  with ∼ 1% deviation in accuracy from current SotA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Ancilia is designed from the ground up to respect the pri-  vacy of the people and communities being surveilled.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia  does not store any personally identifiable information in any  databases and does not make use of invasive artificial intelli-  gence techniques such as facial recognition or gait detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Ancilia strictly provides a pose and locational information for  high-level tasks (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' action recognition, anomaly detection),  as opposed to identity information, which is common.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilla  arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='03561v1  [cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='CV]  9 Jan 2023  0IEEE INTERNET OF THINGS JOURNAL  2  looks at what a person is doing, not who they are.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This allows  Ancilia to act as a buffer to help remove biases based on  race, ethnicity, gender, age, and socio-economic factors, which  can lead to a reduction in the unnecessary conflict between  authorities and marginalized communities that has become  increasingly problematic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' After data is processed on edge and  sent to the cloud for communication and service management  with user devices.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' A mobile app allows user devices to receive  data from the cloud, including alerts when potential security  concerns arise.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  In summary, this article has the following contributions:  We present Ancilia, the first end-to-end scalable real-  world intelligent video surveillance system capable of  performing high-level cognitive tasks in real-time while  achieving SotA accuracy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  We analyze the ethical concerns of intelligent video  surveillance, both from a privacy and fairness perspective,  and illustrate how Ancilia’s design is purpose-built to  address them.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  We perform an end-to-end empirical evaluation of Ancilia  using two high-level cognitive tasks directly related to  intelligent surveillance, action recognition, and anomaly  detection, investigating the trade-off in accuracy required  to achieve real-time performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  We perform an exhaustive system-level evaluation of  Ancilia’s real-time performance and scalability across  different classes of hardware and increasing scenario  intensities, displaying how Ancilia is able to meet real-  time intelligent security needs in different contexts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  II.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' RELATED WORK  There has been a plethora of research regarding the use  of artificial intelligence for video surveillance [4], [6]–[8].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  [9] proposes the use of region proposal based optical flow to  suppress background noise and a bidirectional Bayesian state  transition strategy to model motion uncertainty to enhance  spatio-temporal feature representations for the detection of  salient objects in surveillance videos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' [10] proposes the use  of a person detector, tracking algorithm, and mask classifier  for tracking pedestrians through surveillance video streams.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  In [4], it is determined that in order to address the latency  concerns of real-time video surveillance, a shift towards edge  computing is needed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Nikouei et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' [11]–[13] explore the  feasibility of using low-power edge devices to perform object  detection and tracking in surveillance scenarios.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' They argue  that in worst case 5 FPS is high enough throughput for tracking  humans in surveillance applications, and as such computation  can be pushed to the edge.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' However, their results show that  even light weight convolutional neural networks can prove  problematic for low-power devices, often reducing throughput  below the 5 FPS threshold.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' [14] proposes a system using low-  power embedded GPUs to perform detection, tracking, path  prediction, pose estimation, and multi-camera re-identification  in a surveillance environment, while placing a focus on real-  time execution and the privacy of tracked pedestrians.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' [15]  proposes a similar system, focusing solely on object detec-  tion, tracking, and multi-camera re-identification to increase  throughput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' [16] proposes using a combination of lightweight  object detection models on the edge and more computation-  ally expensive models in the cloud, splitting computation  between the two to provide real-time video surveillance in  a construction site environment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' [17] proposes the use of  background detection, vehicle detection, and kalman filter [18]  based tracking for parking lot surveillance and determining lot  occupancy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' [19] proposes a system that uses object detection,  person tracking, scene segmentation, and joint trajectory and  activity prediction for pedestrians in a surveillance setting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  The future of intelligent surveillance is heading towards  systems able to perform high-level cognitive tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' A recent  survey focusing on real-world video surveillance [4] asserts  that while the domain of video surveillance is comprised  of understanding stationary object, vehicles, individuals, and  crowds, the ability to determine when anomalous events oc-  cur is paramount for intelligent surveillance systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Other  research has supported this assertion [6].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' [20] utilizes the  Infinite Hidden Markov Model and Bayesian Nonparametric  Factor Analysis to find patterns in video streams and detect  abnormal events.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' [21] proposes active learning and fuzzy  aggregation to learn what constitutes an anomaly continually  over time, adapting the scenarios not seen in standard datasets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  [22] proposes a system to detect suspicious behaviors in a  mall surveillance setting, using lightweight algorithms such  as segmentation, blob fusion, and kalman filter based tracking  [18].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' AnomalyNet [23] is a recently proposed recurrent neutral  network with adaptive iterative hard-thresholding and long  short-term memory that works directly off pixel information to  eliminate background noise, capture motion, and learn sparse  representation and dictionary to perform anomaly detection in  video surveillance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  III.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' ETHICAL CONCERNS  Video surveillance has always been associated with social  and ethical concerns, whether in traditional form or more  recent intelligent formats.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Respecting citizens’ privacy and  autonomy while improving public safety and security are the  most well-known and enduring ethical issues in this context  [24]–[27].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Developing a successful smart video surveillance  solution that addresses the public safety problem and engages  the community up to a certain level is only possible by  considering these concerns.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  There is rising attention among scholars to the issue of  incorporating privacy concerns at the design level, referred to  as ”privacy by design” [28].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The source of discrimination and  privacy violation in many data-driven and AI-based systems,  such as Smart video surveillance technology, is using Personal  Identifiable Information (PII) [29], [30].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Using PII, such as  actual footage of people’s daily activities at any stage of the  technology, can increase the risk of privacy violation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' There is  a long-lasting debate on the ethical challenges of using facial  recognition technologies in different sectors and how using  this technology can result in privacy violation [31]–[34].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Avoiding facial recognition technologies does not guarantee  the system is entirely privacy persevering.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Storing images of  pedestrians is another source of ethical violation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' From the  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='IEEE INTERNET OF THINGS JOURNAL  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='3  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='Neural  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='Network  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='Filter  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='Match and  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='Combine  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='Algorithm  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='(A) Local Node 0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='P  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='P,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='IDL  C  P,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='IDL  BBP  FL  D  Edge  Boundary  Video  Stream  from  Camera  (D) User  Device(s)  AR  R  User  Device  Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia algorithmic details.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' N local nodes are connected to a single global node on the edge.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The final analyses are transferred to the cloud node to  feed the application on the user device.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Multiple edges may be connected to the could, though this figure only shows one edge for clarity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' BBP , BBO, IDL,  P, C, FL, D, FD, IDG, I, SA, R, and AR refer to bounding boxes for pedestrians, bounding boxes of objects, local identities, poses, person crops that  passed selection, features from the local node, data from the downstream tasks, features from the database, global identities, information from the database,  completed statistical analysis, requests from users, and requested attributes respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  discrimination perspective, using any form of PII can con-  tribute to the issue of marginalization in policing systems [35].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Therefore, an essential step in designing a non-discriminatory  system is to ensure the system is not dependent on PII.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  This requires a specific approach toward the design of such  technology in the choice of algorithm, the type of data used,,  and the storing of such data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Ancilia addresses this by not storing any PII or sending any  PII across the network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Such data is destroyed after it is used.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Ancilia utilizes pose-based methods for all high-level cognitive  tasks, ensuring no PII is ever used in such algorithms.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This  allows for such processing to occur without any potential  for gender, ethnicity, or class-based discrimination.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' As such,  Ancilia is able to address the ethical challenge of privacy in  smart video surveillance systems while also addressing the  ethical issue of discrimination.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  IV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' ANCILIA ALGORITHMIC FRAMEWORK  The algorithmic core of Ancilia is separated into two  conceptual systems: the local nodes containing the algorithmic  pipeline of each camera and the global node that handles  all processing that requires understanding of multiple camera  perspectives.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' These two systems make up the algorithmic core  of Ancilia and are the basis on which all higher understanding  is achieved.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' A visual representation of this algorithmic core  can be seen in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Single Camera Vision Pipeline  As seen in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 2, the local algorithmic pipeline starts when  an image is extracted from the camera.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The image is first run  through an object detector to locate people, vehicles, animals,  and other important objects in the scene.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This is important not  only because it acts as the basis for the rest of the algorithmic  pipeline but also because it can be used for basic situational  awareness.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Sometimes, just the presence of a certain object  in a scene is noteworthy, like a person in an unauthorized  location, a bag left unattended, or the presence of a firearm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Ancilia uses YOLOv5 [36] for this purpose (however, it can be  any detector).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The locational coordinates of persons are sent to  a tracker, where tracklets are created, matching each person  with their previous detections in prior images.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia uses  ByteTrack [37].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The tracking allows for understanding how  a person moves throughout a scene, which is vital for many  surveillance applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' It also allows Ancilia to understand  which poses belong to which persons over time, which is  vital for many high-level tasks that provide much-needed  situational awareness.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Image crops of the people detected in  the image are also sent to a human pose estimator, where two-  dimensional pose skeletons are created.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia uses HRNet  [38] for extracting 2D skeletons.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Using pose data for higher-  level tasks has two major benefits over simply using raw pixel  data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' First, pose data is of much lower dimensionality than  pixel data, making it much less computationally expensive and  allowing the Ancilia to function in real-time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Second, pose  data contains absolutely zero identifiable information, making  it impossible for high-level tasks to form unintended biases  based on ethnicity, gender, age, or other identity-based metrics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Multi-Camera Person Re-identification  While the tracker tracks people within a single camera,  locational information cannot accurately re-identify a person  across multiple cameras.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' For this, the same person crops that  were sent to the human pose estimator are also sent to a person  re-identification feature extractor, where an abstract feature  representation is created for each person.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Only one feature  representation is created for each person over a period of 30  frames, and only when the quality of the representation can  be assured, as poor quality representations are detrimental to  accurate multi-camera person re-identification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia uses a  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='feature representation filtering algorithm to verify two qualities  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='Task 0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='Task 1  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='Feature  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='Local Node 0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='β3  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='β2  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='β3  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='β1  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='β1  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='β1  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='δ0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='δ1  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='δN-1  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='δ0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='δ1  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='δN-1  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' A detailed view of system design in Ancilia’s local nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' β and δ refer to different batch sizes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' FL and D represent local features and data received  from downstream tasks respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  for person crops.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' First, a person crop must contain a high-  quality view of the person.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' To this end, the filter algorithm uses  the 2D pose skeleton and verifies that at least 9 keypoints were  detected with at least 60% confidence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The filter algorithm  looks at the overlap (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Intersection of Union) of the bounding  boxes generated by the object detector.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' An individual’s bound  box must have an Intersection over Union (IoU) of no more  than 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='1 with any other person.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' If those two conditions are met,  the person crop is determined to be of high enough quality to  produce an adequate feature representation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' If more than one  crop is deemed suitable for a single person during a 30 frame  window, the one with the most confident pose is selected.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The  features created by the feature extractor are sent to the global  node for multi-camera person re-identification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia uses  OSNet [39] to extract feature representations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' higher Level Tasks  High-level tasks are executed on the local node, and have  access to the object, tracking, and pose data generated in the  previous steps.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Since the decision of which high-level tasks are  needed is highly application dependent, we do not consider  these tasks to be part of the Ancilia algorithmic core, but  instead an extension to be customized based on intended use.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  In this paper, we use action recognition and anomaly detection  as two common examples of high-level tasks that are highly  relevant to intelligent surveillance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' For action recognition, we  choose PoseConv3D [40] and CTR-CGN [41], two state-of-  the-art networks that can utilize the 2D human pose skeletons  provided by Ancilia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' For anomaly detection, we use GEPC  [42] and MPED-RNN [43], which are based on 2D human  pose skeletons.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' SYSTEM DESIGN  Beyond the algorithmic design, Ancilia can be analyzed  from a system-level design and implementation perspective.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  The local node in particular has a complex system design, as  seen in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The global node and cloud are much simpler,  as shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Parallelism  A key design objective of Ancilia is to achieve higher  efficiency by balancing throughput and latency.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia uses  pipelining to take advantage of process parallelism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Each  major task is implemented as a separate process, which  executes concurrently with other processes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' These processes  communicate with each other using queues to utilize memory  resources better and enable fast inter-process communication.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  While pipelining is a well-known technique for optimization,  the overhead associated with its implementation means a  balance needs to be found.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Figure 3 shows a detailed view of  the system design on the local node.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Each pipeline stage is sep-  arated by a queue with a size limit of λ1 elements, preventing  any potential overflow from uneven execution speed between  pipeline stages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' By default, Ancilia uses a λ1 value of 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' As  is common, Ancilia offloads highly parallel tasks that rely on  neural networks (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' object detection, pose estimation, feature  extraction, and many high-level tasks) to Graphics Processing  Units (GPUs) for execution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Data Batching  Batching is another technique Ancilia implement to better  utilize hardware resources.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Generally, batching is able to  greatly increase the throughput of a system at the cost of end-  to-end latency.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' However, many high-level tasks (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' action  IEEE INTERNET OF THINGS JOURNAL  5  recognition, anomaly detection) require multiple video frames  worth of input data (often called a window) before the can start  processing, so the latency that would be incurred by batching  input frames is already inherent in these high-level task, as  long as the frame batch and high-level task window are of  the same size.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Further, as frame batching ultimately increases  the throughput, the end-to-end latency is decreased when  compared processing each frame sequentially.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' While object  detection works on entire frames, all other neural networks in  Ancilia work off individual objects.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' These objects are batched  together before being input to the network, greatly increasing  hardware utilization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' There can be multiple object batches  within a single frame batch, based on how many of the relevant  objects are detected in the video.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Local Node  Once the local node receives the video stream from the  camera, the proprocessor is responsible for all basic image  processing necessary before sending the frames through the  algorithmic core.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' After preprocessing, frames are batched in  sequential segements of size β1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia sets β1 = 30 to match  the window size of high-level tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This is also convenient  as most modern security and IoT cameras record video at  either 30 or 60 FPS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The batched frames are sent to the object  detector, which outputs a list of objects with class labels and  bounding box coordinates.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Bounding boxes for pedestrians are  sent to the tracker, while bounding boxes for other objects  are passed through the system for use in high-level tasks  and statistical analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' A crop of each pedestrian from the  original frame is passed through to the pose estimator.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' At  the tracker, bounding boxes for pedestrians are unbatched  to fit the tracker’s sequential operation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The tracker groups  the pedestrians and either matches them with previously seen  pedestrians or assigns them a unique local ID.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Afterwards,  the pedestrians are once again batched by frame and sent to  the pose estimator.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' At the pose estimator, the object batching  is performed on the person crops, with a batch size of  β2 = 32.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' These batches are fed to the pose estimator, which  outputs human pose skeletons for each person crop.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Then the  pedestrian bounding boxes, person crops, local IDs, and human  pose skeletons are once again batched by frame and combined  with the object bounding boxes from the object detector.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  The pedestrian bounding boxes, person crops, local IDs, and  pose skeletons are sent to crop selection, while the pedestrian  bounding boxes, object bounding boxes, local IDs, and pose  skeletons are sent to each high-level task as necessary.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' While  the person crops are necessary for the feature extraction that  enables multi-camera re-identification, no identifiable data is  sent to any of the high-level tasks, keeping in line with the  ethical concerns mentioned in Sec.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' III.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Crop selection filters out low-quality person crops based on  bounding box overlap and keypoint confidence, as described  in Sec.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' IV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The remaining crops are then batched, with size β3  being dynamic based on the number of persons in the scene,  and sent to the feature extractor.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Once features are extracted,  the are sent for transfer to the server.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Each high-level task  receives data at the granularity of a frame batch, and sends  data to the server at whatever granularity that task requires.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Each high-level task has its own process and works in parallel  with other tasks as well as with crop selection and feature  extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Communication is completely decoupled from the  pipeline, so once the data is sent the local node pipeline  continues to function as normal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Importantly, no identifiable  information is ever sent to the global node, keeping in line  with the privacy and ethical concerns mentioned in Sec.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' III  D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Global Node  All received data is stored in a relational database on the  global node.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The matching algorithm described in Sec.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' IV  compares the received features with existing features in the  database over the period λ5 and assigns a global ID based on  the results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The default value for lλ5 is set to 1 hour, but this  should be changed to suit the needs of the application.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' An  assortment of algorithms performs statistical analysis using  the relational database, as detailed in Sec.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' IV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The analysis  is transmitted to the cloud node using APIs provided by the  cloud service provider.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' By default, Ancilia uses Amazon Web  Services, but this can be altered based on user/application  needs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The cloud (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Amazon Web Services (AWS)) receives  analyzed data from the global node.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  VI.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' EXPERIMENTAL RESULTS  A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Algorithmic Core  The algorithmic core of Ancilia consist of multiple algo-  rithms, each of which works off of data generated by the  previous algorithms.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' As these algorithms leverage imperfect  neural networks, they generate noise that accumulates through  the system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' To understand the source of this noise, we must  first look at the accuracy of each of these core algorithms  in isolation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Table I shows the accuracies of the algorithmic  core’s four main tasks: object detection, pedestrian tracking,  human pose estimation, and person re-identification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The table  also shows the accuracies of the top SotA models in each  task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' These SotA methods are not suitable for intelligent  surveillance applications, as their excessive computation and  vast parameters make real-time execution impossible, but the  comparison allows us to see the maximum potential allowable  by current research and the accuracy loss incurred to keep  Ancilia performing in real-time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  TABLE I  ACCURACY OF ANCILIA’S ALGORITHMIC CORE NETWORKS IN  ISOLATION.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' ACCURACIES OF OBJECT DETECTION AND POSE ESTIMATION  ARE USING THE COCO DATASET [44], TRACKING USING MOT20 [45],  AND PERSON REID USING DUKEMTMC [46].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' SOTA REPRESENT THE  HIGHEST ACCURACIES CURRENTLY ACHIEVABLE WITH  STATE-OF-THE-ART METHODS WHEN COMPUTATION AND LATENCY ARE  NOT A CONCERN.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Task  Method  Metric  Accuracy  SotA  Object Detection  YOLOv5 [36]  mAP  49.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='0  65.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='0 [47]  Tracking  ByteTrack [37]  MOTA  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='8  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='9 [48]  Pose Estimation  HRNet [38]  AP  75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='1  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='1 [49]  Person ReID  OSNet [39]  Top-1  88.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='6  95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='6 [50]  Object detection sees the biggest hit to accuracy, with a 16%  drop from SotA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This is intuitive, as YOLOv5 [36] is not only  IEEE INTERNET OF THINGS JOURNAL  6  the largest model in the algorithmic core, but also the only  one that operates on the raw camera stream.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' So while larger  models are available and would be able to produce higher  accuracy, even a slight increase in model size or computation  would result in a noticeable decrease in throughput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Human  pose estimation sees a decrease in accuracy for a similar  reason, though much smaller in scale at only 6%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' While  HRNet [38] is not run on the raw camera stream, it is run  individually for each person detected by the object detector.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  As such, maintaining a small model size is preferable.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Person  re-identification sees a slightly larger drop in accuracy than  human pose estimation at 7%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' While this is partly due to  using a lightweight model, OSNet [39], the SotA model for  person reID is also lightweight.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' However, the SotA uses a  centroid based retrieval method not suitable for pen-set reID,  of which most surveillance scenarios are.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Pedestrian tracking  sees almost no drop in accuracy, approximately 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='1%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This  stems from the comparative ease of tracking pedestrians in  a single camera, where a simple, lightweight algorithm like  ByteTrack [37] see almost no performance difference from  the top of the line SotA approaches.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' High-level Tasks  To better understand how the noise generated by the al-  gorithmic core effects overall performance, and thus how  well Ancilia performs in the realm of real-world intelligent  surveillance, we examine the performance of two high-level  cognitive surveillance tasks when running on Ancilia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' For  Ancilia to be a benefit to intelligent surveillance tasks, we  must ensure that excess false alarms or missed positive events  do not occur.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' To assess this, we choose action recognition  and anomaly detection, as these tasks can utilize the human  pose information generated by the algorithmic core, resulting  in faster and less biased inference.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Since both these methods  utilize temporal batches of human poses for each individual,  these experiments will directly reflect the quality of the object  detection, tracking, re-identification, and pose estimation data  generated by Ancilia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  1) High-level Task - Action Recognition: We select two  state-of-the-art action recognition models, PoseConv3d [40]  and CTR-GCN [41], and train them using data generated with  Ancilia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' For each model, we train and test with full (30 FPS)  and half (15 FPS) throughput on NTU60-XSub [51].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Both  models use a window size of 30 and are trained for 24 epochs  using Stochastic Gradient Descent (SGD) with a momentum of  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='9 and Cosine Annealing scheduling.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' PoseConv3d and CTR-  GCN have weight decay of 3e−4 and 5e−4 and an initial  learning rate of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='4 and 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='2, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  The results of these experiments can be seen in Tab.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' II.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  We report the Top-1 and Top-5 accuracy and compare the  results using data generated by Ancilia to the original data  available through the PYSKL toolbox [52].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' We can see that  Ancilia is able to provide data of comparable quality to the  original;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' action recognition as a high-level task in Ancilia sees  around 1% drop in accuracy compared to the original data  using PoseConv3D [40] at full throughput, and around 3% at  half throughput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Using CTR-GCN [41], Ancilia sees a 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='5%  TABLE II  TOP-1 AND TOP-5 ACCURACIES ON NTU60-XSUB [51] IN FULL AND  HALF THROUGHPUT MODES FOR POSECONV3D [40] AND CTR-GCN  [41].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Model  Data  FPS  Top-1 (%)  Top-5 (%)  PoseConv3D [40]  [52]  15  91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='96  99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='47  30  92.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='76  99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='57  Ours  15  88.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='79  98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='82  30  91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='99  99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='28  CTR-GCN [41]  [52]  15  86.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='36  98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='46  30  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='07  98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='26  Ours  15  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='58  97.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='52  30  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='44  97.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='2  drop at full throughput and a 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='8% drop at half throughput,  compared to the original data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' From this we can infer that  PoseConv3D is more robust to noise than CTR-GCN, however  both performed reasonably well with data generated from  Ancilia, demonstrating its efficacy for intelligent surveillance  applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Another interesting observation is that CTR-GCN [41]  actually performed noticeably better at half throughput than  at full throughput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This means that CTR-GCN is more suited  to taking advantage of the higher temporal window allowed  when using half throughput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This is something to consider  when choosing an action recognition model when a real-time  throughput of 30 FPS cannot be guaranteed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  2) High-level Task - Anomaly Detection: Using the Shang-  haiTech dataset [53] we train two state-of-the-art anomaly  detection models, GEPC [42] and MPED-RNN [43], using  both data generated by Ancilia and the data provided by the  original authors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The same training strategy from Sec.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' VI-B1  is used, with both models trained in full (20 FPS) and half (10  FPS) modes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' GEPC is trained for 25 epochs with a window  size of 30 and stride of 20 using Adam optimizer with a  learning rate of 1e-4, weight decay of 1e-5, and batch size  of 512.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' MPED-RNN is trained with an input window size of  30, a reconstruction window of 12, and a prediction window of  6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The model is trained for 5 epochs using the Adam optimizer  with a learning rate of 1e−3 and a batch size of 265.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  TABLE III  AUC ROC, AUC PR, AND EER ON SHANGHAITECH DATASET [53] IN  FULL AND HALF THROUGHPUT MODES FOR GEPC [42] AND MPED-RNN  [43].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Model  Data  FPS  AUC ROC  AUC PR  EER  GEPC [42]  [42]  10  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='31  Ours  10  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='32  MPED-RNN [43]  [43]  10  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='36  Ours  10  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='37  20  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='37  The results of this experiment can be seen in Tab.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' III.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  In line with current practices, we report Area Under the  Receiver Operating Characteristic Cure (AUC ROC), Area  IEEE INTERNET OF THINGS JOURNAL  7  1  2  3  4  5  6  7  8  0  10  20  30  40  50  60  Normal  Heavy  Extreme  Nodes  Throughput (FPS)  (a) Server A  1  2  3  4  5  6  7  8  15  20  25  30  Normal  Heavy  Extreme  Nodes  Throughput (FPS)  (b) Server B  1  2  3  4  5  6  7  8  0  10  20  30  40  50  60  Normal  Heavy  Extreme  Nodes  Throughput (FPS)  (c) Workstation  Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Throughput of Ancilia across different crowd densities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Hardware details can be seen in Tab.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' IV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  TABLE IV  SYSTEM CONFIGURATIONS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' STATS ARE PER CPU/GPU OF THE LISTED TYPE.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Processor  GPU  Name  Model  Cores  Clock Speed  Model  CUDA Cores  VRAM  Server A  2× EPYC 7513  32  2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='6 GHz  4× V100  5120  32 GB  Server B  2× Xeon E5-2640 v4  10  2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='4 GHz  2× Titan V  5120  12 GB  Workstation  Threadripper Pro 3975WX  32  3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='50 GHz  3× A6000  10752  48 GB  Under the Precision-recall Curve (AUC PR), and the Equal  Error Rate (EER).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' With GEPC, we can see that Ancilia more  than measures up to the task, with only a 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='5% drop in AUC  ROC at full throughput and less than a 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='2% drop in AUC ROC  at half throughput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' AUC PR shows a more substantial drop  of 4% at full throughput, but goes down to less than 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='5% at  half throughput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Equal Error Rates are almost identical, seeing  almost no change (less than 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='01) when using Ancilia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' MPED-  RNN, which displayed lower overall accuracy in all regards to  begin with, sees a more significant drop in AUC ROC at full  throughput, losing 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='5%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' However, at half throughput the AUC  ROC actually increases when using Ancilia, though only by  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='5%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The AUC PR results mirror that of GEPC, dropping  3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='8% at full throughput and 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='7% at half throughput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The  Equal Error Rates are once again nearly identical.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Being able  to perform a high-level task such as anomaly detection while  maintaining accuracies so close to current SotA in research,  demonstrates Ancilia’s ability to produce quality data, suitable  for intelligent surveillance applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Real-time System Performance  Algorithmic accuracy is vital for ensuring the information  provided by high-level cognitive tasks is beneficial for surveil-  lance applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' However, Ancilia’s ability to perform in  real-time is equally important.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' We conduct a series of ex-  periments, evaluating the runtime performance of Ancilia on  different hardware, with different scenario intensities, and for  increasing number of local nodes per hardware device.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' We  focus on the performance of the local node, as the global node  is completely decoupled from the algorithmic pipeline and has  no noticeable effect on throughput or latency.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  We choose three different hardware configurations for these  experiments: a high-end server, a lower-end server, and a high-  end workstation, as seen in Tab.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' IV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' For our scenarios, we  use the DukeMTMC-video dataset [46] and pick three scenes  with different crowd densities: normal density, heavy density,  and extreme density.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The distribution of detection density  in each scenario can be seen in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Note that what is  considered ”normal density” will change based on application  environment, which is why we report on such a wide range.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Each video lasts for  32k frames, with  7k frames warm-up  and cool-down.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' We test using 1, 2, 4, 6, and 8 local nodes on  a single system, showing how throughput and latency scale  in such cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Each experiment is conducted three times, the  throughput and latency averaged across runs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The results of  these experiments can be seen in Tab.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' V and Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The  distribution of throughput in these scenes can be seen in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Under normal crowd density, Server A and Workstation  are both able to achieve over 50 FPS with a single local  node, with an end-to-end latency of 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='39 and 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='90 seconds  respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This is well above the 30 and 20 FPS required  by action recognition and anomaly detection algorithms at  full throughput, and the latency is low enough to be suitable  for most surveillance applications where the main concern is  notify authorities in time for appropriate response.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Server A  is able to handle 6 local nodes in the normal scenario while  maintaining above 30 FPS, while Workstation can do so with  4 nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Server A is able to maintain above 26 FPS while  running all 8 local nodes, while Workstation drops to just  below 18 FPS at 8 local nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Server B falls just short of  30 FPS even with a single node in normal crowd density.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  However, it is able to maintain above 20 FPS while handling  two nodes simultaneously.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Due to having only a single GPU  and limited VRAM, Server B was unable to run 4 or more  nodes concurrently.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Heavy crowd density proves more challenging, with both  Server A and Workstation only able to achieve above 30  FPS with up to 4 nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The end-to-end latency is also  longer than it was under normal crowd density, with Server  A seeing almost double the latency and Workstation seeing  around a 20% to 80% increase in most cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Server A and  Workstation are able to mainatin above 20 FPS at 8 and 6  IEEE INTERNET OF THINGS JOURNAL  8  TABLE V  AVERAGE THROUGHPUT AND LATENCY WHEN RUNNING MULTIPLE LOCAL NODES ON A SINGLE SERVER.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Server A  Server B  Workstation  Crowd Density  Nodes  FPS  Latency (s)  FPS  Latency (s)  FPS  Latency (s)  Normal  (70 detections  per second)  1  52.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='94  5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='39  29.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='76  9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='73  54.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='91  4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='90  2  48.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='99  5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='97  22.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='82  nodes respectively, while Workstation drops to just above 15  FPS at 8 nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Interestingly, with heavy crowd density we  start to see unusual behavior with Server A having worse  performance with a singe node than it does with 2 nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  This is caused by the abundance of CPU and GPU resources  available to the server and a single node being unable to  fully utilize them.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' As such, the behavior of Server A in the  heavy and extreme crowd density scenarios does not start to  match the expected behavior and mimic the other systems until  multiple nodes are being run simultaneously.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This behavior  is not too concerning, considering it does not make sense  to purchase such a high-end server class machine for only  running a single local node, when a more latency focused  workstation would be both cheaper and more effective.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Server  B behaves similarly to how it did with normal crowd density,  except that it falls slightly below 20 FPS when running two  nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Assuming only half throughput was needed for high-  level tasks, Server B would still be suitable for running up to  two nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  With the extreme crowd density scenario, Ancilia begins  to struggle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' None of the systems are able to achieve above  30 FPS even with a single camera, putting full throughput  action recognition out of reach.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Server A is able to achieve  above 20 FPS with 2 nodes (but notably not with 1) and  Workstation is able to do so with 1 or 2 nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Both Server  A and Workstation can maintain above 15 FPS at 4 nodes,  but both drop to around 9 and 6 FPS at 6 and 8 nodes,  respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' [54] argues that 5 FPS is suitable for tacking  pedestrians, and while that is true, high-level tasks that rely  on detailed human motion, such as action recognition and  anomaly detection, often struggle for accuracy when running  below 10 FPS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Another issue is with the increased latency.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Running only 1 node, Server A and Workstation have latencies  of 36 seconds and 25 seconds respectively, which is suitable  for many surveillance applications, but might be too much  for those that require sharper response times.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' The latency  increase to over 2 minutes for both systems with 8 nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Combined with the low throughput, it becomes difficult to  recommend running more than 4 nodes on a single system with  Ancilia when operating under extreme crowd density, expect  0  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='05  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='1  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='15  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content='45  0  5  10  15  20  Extreme  Heavy  Normal  Average Number of Detections/Batch  Probability  0  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='02  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='04  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='06  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='08  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='1  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='12  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='14  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='16  20  30  40  50  60  70  80  90  Extreme  Heavy  Normal  Throughput (FPS)  Probability  Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  The performance of Ancilia in terms of throughput and detection  distribution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  for applications where low throughput and high latency are  not as much of a concern.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Server B is unable to achieve 15  FPS, but does stay above 10 FPS for both 1 and 2 nodes,  making it suitable for half throughput in anomaly detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  However, the latencies of 44 and 48 seconds might be too  much for some applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' This is the extreme scenario, and  it understandably provides quite the challenge for real-time  execution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Overall, Ancilia is able to meet the needs of high-level cog-  nitive tasks while still achieving performance suitable for real-  IEEE INTERNET OF THINGS JOURNAL  9  time intelligent surveillance applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Exact performance is  dependent on both the hardware used and the intensity of the  scene, but these results show that even for the most extreme of  scenarios, Ancilia can be used to provide intelligent assistance  to surveillance applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  VII.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' CONCLUSION  In this article we presented Ancilia, an end-to-end scal-  able intelligent video surveillance system for the Artificial  Intelligence of Things.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Through empirical evaluation, Ancilia  has demonstrated its ability to bring state-of-the-art artificial  intelligence to real-world surveillance applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Ancilia  performs high-level cognitive tasks (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' action recognition and  anomaly detection) in real-time, all while respecting ethical  and privacy concerns common to surveillance applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  ACKNOWLEDGMENTS  This research is supported by the National Science Foun-  dation (NSF) under Award No.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 1831795 and NSF Graduate  Research Fellowship Award No.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 1848727.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  REFERENCES  [1] C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
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+page_content=' Wang, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Chen, and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Lin, “Pyskl: Towards good  practices for skeleton action recognition,” 2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' [Online].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Available:  https://arxiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='org/abs/2205.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='09443  [53] W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Liu, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Luo, and S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Gao, “Future frame prediction for anomaly  detection – a new baseline,” in 2018 IEEE Conference on Computer  Vision and Pattern Recognition (CVPR), 2018.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  [54] L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Wang, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Huynh, and P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Koniusz, “A comparative review of  recent kinect-based action recognition algorithms,” IEEE Transactions  on Image Processing, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 29, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' 15–28, 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  BIOGRAPHY  Armin Danesh Pazho (S’22) is currently a Ph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  student at the University of North Carolina at Char-  lotte, NC, United States.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' With a focus on Artificial  Intelligence, Computer Vision, and Deep Learning,  his research delves into the realm of developing AI  for practical, real-world applications and addressing  the challenges and requirements inherent in these  fields.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Specifically, his research covers action recog-  nition, anomaly detection, person re-identification,  human pose estimation, and path prediction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Christopher Neff (S’18) is a National Science  Foundation Graduate Research Fellow and Doctoral  Candidate at the University of North Carolina at  Charlotte.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' His dissertation focus is on tackling the  challenges of bringing human-centric computer vi-  sion to real-world applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' His previous work  focuses on person re-identification, human pose es-  timation, action recognition, real-time system devel-  opment, lightweight algorithms, noisy data, domain  shift, and real-world applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Ghazal Alinezhad Noghre (S’22) is currently pur-  suing her Ph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' in Electrical and Computer Engi-  neering at the University of North Carolina at Char-  lotte, NC, United States.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Her research concentrates  on Artificial Intelligence, Machine Learning, and  Computer Vision.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' She is particularly interested in the  applications of anomaly detection, action recogni-  tion, and path prediction in real-world environments,  and the challenges associated with these fields.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Babak Rahimi Ardabili is a Ph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' student in the  Public Policy Analysis program at the University  of North Carolina at Charlotte, United States.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' His  main research area is emerging technologies policy  making.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' He mainly focuses on the intersection of  Artificial Intelligence and policy from a privacy  perspective and the challenges of bringing the tech-  nology to the community.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Shanle Yao is an Electrical Engineering Graduate  student from the University of North Carolina at  Charlotte.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' His dissertation focus is on optimization  and application of Computer Vision pipeline perfor-  mance and throughput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' His areas of interest include  object detection, multiple objects tracking, human  pose estimation, semantic segmentation and real-  world applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Mohammedreza Baharani is an ML researcher and  edge system deployment engineer at ForesightCares.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  He received his Ph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' in computer engineering in  2021 from the University of North Carolina at  Charlotte, USA, and was a postdoctoral researcher  at the TeCSAR Lab.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' His research focuses on the  intersection of computer architecture engineering  and machine learning, with the goal of enabling AI  algorithms on edge devices to have a positive impact  in fields such as healthcare.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Hamed Tabkhi (S’07–M’14) is an Associate Pro-  fessor in the Department of Electrical and Com-  puter Engineering, University of North Carolina at  Charlotte, USA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' He was a post-doctoral research  associate at Northeastern University.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Hamed Tabkhi  received his Ph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' degree in 2014 from Northeast-  ern University under the direction of Prof.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Gunar  Schirner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content=' Overall, his research focuses on transfor-  mative computer systems and architecture for cyber-  physical, real-time streaming and emerging machine  learning applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  Award1831795  d PI), Shannon Reid, Dougl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}
+page_content='  er,Robert Phocas, Arun Ravi  leta,ChristopherNeff,James  &Integrative  Communit  oroach  ervice of Public  t"policing  edge  tiple  co' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CtE1T4oBgHgl3EQf9wbT/content/2301.03561v1.pdf'}