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README.md
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# Accident Detection Model
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This application showcases the capabilities of our Accident Detection Model, a pivotal component of our research project focused on Accident Detection within Smart City Transportation frameworks.
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## Overview
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The application empowers users to view a selection of sample accident videos and upload a new video to test the model. Our model is adept at detecting accidents in both trimmed and untrimmed video formats.
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## Table of Contents
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- [Installation](#installation)
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- [Usage](#usage)
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- [Features](#features)
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- [Contribution](#contribution)
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- [License](#license)
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- [Acknowledgments](#acknowledgments)
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## Installation
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1. **Clone the repository:**
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```bash
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git clone [(https://github.com/adewopova/Accident_detection_SM_City/)]
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```
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2. **Navigate to the directory:**
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```bash
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cd path_to_diretory
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```
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3. **Install the required dependencies:**
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```bash
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pip install -r requirements.txt
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```
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4. **Launch the Streamlit app:**
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```bash
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streamlit run app.py
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```
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## Usage
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With the app up and running:
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- Opt between trimmed and untrimmed video variants.
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- Pick a sample video from the provided list or upload a video of your choice.
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- The model will analyze the video and superimpose accident likelihood indicators.
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## Features
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- **Sample Videos**: Preloaded sample videos for immediate testing.
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- **Accident Prediction**: The core functionality that exhibits the probability of an accident occurrence within the selected video.
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- **User-friendly Interface**: Crafted using Streamlit, ensuring a seamless and intuitive user experience.
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## Contribution
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Your contributions can make a difference! Kindly consult the contribution guidelines prior to submitting any changes.
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## License
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This project is protected under the MIT License. For more details, please refer to the `LICENSE.md` file.
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## Acknowledgments
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A heartfelt appreciation to our dedicated research team members: Victor Adewopo and Nelly Elsayed.
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[https://arxiv.org/pdf/2310.10038.pdf](#)
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