OmniFall: A Unified Benchmark for Staged-to-Wild Fall Detection

This repository contains the annotation and split definitions for OmniFall, a comprehensive benchmark that unifies eight public indoor fall datasets under a consistent ten-class annotation scheme, complemented by the OOPS-Fall benchmark of genuine accidents captured in the wild.

Paper: OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection

Overview

Falls are the leading cause of fatal injuries among older adults worldwide. While the mechanical event of falling lasts only a fraction of a second, the critical health risk often comes from the ensuing "fallen" state—when a person remains on the ground, potentially injured and unable to call for help.

OmniFall addresses three critical limitations in current fall detection research:

1. Unified Taxonomy: Rather than binary fall/no-fall classification, we provide a consistent ten-class scheme across datasets that distinguishes transient actions (fall, sit down, lie down, stand up) from their static outcomes (fallen, sitting, lying, standing).

2. Combined Benchmark: We unify eight public datasets (14+ hours of video, 112 subjects, 31 camera views) into a single benchmark with standardized train/val/test splits.

3. In-the-Wild Evaluation: We include OOPS-Fall, curated from genuine accident videos of the OOPS dataset to test generalization to real-world conditions.

Datasets

This benchmark includes annotations for the following datasets:

1. CMDFall (7h 25m single view) - 50 subjects, 7 synchronized views
2. UP Fall (4h 35m) - 17 subjects, 2 synchronized views
3. Le2i (47m) - 9 subjects, 6 different rooms
4. GMDCSA24 (21m) - 4 subjects, 3 rooms
5. CAUCAFall (16m) - 10 subjects, 1 room
6. EDF (13m) - 5 subjects, 2 views synchronized
7. OCCU (14m) - 5 subjects, 2 views not synchronized
8. MCFD (12m) - 1 subject, 8 views
9. OOPS-Fall - Curated subset of genuine fall accidents from the OOPS dataset, strong variation in subjects and views.

Structure

The repository is organized as follows:

• labels/ - CSV files containing frame-level annotations for each dataset as well as label2id.csv
• splits/ - Train/validation/test splits for cross-subject (CS) and cross-view (CV) evaluation
  • splits/cs/ - Cross-subject splits, where training, validation, and test sets contain different subjects
  • splits/cv/ - Cross-view splits, where training, validation, and test sets contain different camera views

Label Format

Each label file in the labels/ directory follows this format:

path,label,start,end,subject,cam,dataset
path/to/clip,class_id,start_time,end_time,subject_id,camera_id,dataset_name

Where:

• path: Relative path to the video, given the respective dataset root.
• label: Class ID (0-9) corresponding to one of the ten activity classes:
  • 0: walk
  • 1: fall
  • 2: fallen
  • 3: sit_down
  • 4: sitting
  • 5: lie_down
  • 6: lying
  • 7: stand_up
  • 8: standing
  • 9: other
• start: Start time of the segment (in seconds)
• end: End time of the segment (in seconds)
• subject: Subject ID
• cam: Camera view ID
• dataset: Name of the dataset

For OOPS-Fall, only fall segments and non-fall segments are labeled; non-falls are labels as "other", independent of the underlying content, as long as it is not a fall. Cam and subject ids in OOPS-Fall are -1.

Split Format

Split files in the splits/ directory list the video segments included in each partition. You can use the split paths to filter the label data.:

path
path/to/clip

Evaluation Protocols

We provide multiple evaluation configurations via the dataset.yaml file:

Basic Configurations

• default: Access to all dataset labels (huggingface loads everything into the train split by default.)
• cs: Cross-subject splits for all datasets
• cv: Cross-view splits for all datasets

Individual Dataset Configurations

• caucafall, cmdfall, edf, gmdcsa24, le2i, mcfd, occu, up_fall, OOPS: Access to individual datasets with their respective cross-subject splits

Multi-Dataset Evaluation Protocols

• cs-staged: Cross-subject splits combined across all staged datasets
• cv-staged: Cross-view splits combined across all staged datasets
• cs-staged-wild: Train and validate on staged datasets with cross-subject splits, test on OOPS-Fall
• cv-staged-wild: Train and validate on staged datasets with cross-view splits, test on OOPS-Fall

Examples

from datasets import load_dataset
import pandas as pd

# Load the datasets
print("Loading datasets...")

# Note: We separate segment labels and split definitions, but hugginface datasets always expects a split.
# Thats why all labels are in the train split when loaded, but we create the actual splits afterwards.
labels = load_dataset("simplexsigil2/omnifall", "labels")["train"]

cv_split = load_dataset("simplexsigil2/omnifall", "cv")
cs_split = load_dataset("simplexsigil2/omnifall", "cs")

# There are many more splits, relevant for the paper:
# - cv-staged -> Only lab datasets
# - cs-staged -> Only lab datasets
# - cv-staged-wild -> Lab datasets for train and val, only OOPS-Fall in test set
# - cs-staged-wild -> Lab datasets for train and val, only OOPS-Fall in test set

# Convert to pandas DataFrames
labels_df = pd.DataFrame(labels)
print(f"Labels dataframe shape: {labels_df.shape}")

# Process each split type (CV and CS)
for split_name, split_data in [("CV", cv_split), ("CS", cs_split)]:
    print(f"\n{split_name} Split Processing:")

    # Process each split (train, validation, test)
    for subset_name, subset in split_data.items():
        # Convert to DataFrame
        subset_df = pd.DataFrame(subset)

        # Join with labels on 'path'
        merged_df = pd.merge(subset_df, labels_df, on="path", how="left")

        # Print statistics
        print(f"  {subset_name} split: {len(subset_df)} videos, {merged_df.dropna().shape[0]} labelled segments")

        # Print examples
        if not merged_df.empty:
            print(f"\n  {subset_name.upper()} EXAMPLES:")
            random_samples = merged_df.sample(min(3, len(merged_df)))
            for i, (_, row) in enumerate(random_samples.iterrows()):
                print(f"  Example {i+1}:")
                print(f"    Path: {row['path']}")
                print(f"    Start: {row['start']}")
                print(f"    End: {row['end']}")
                print(f"    Label: {row['label']}")
                print(f"    Subject: {row['subject']}")
                print(f"    Dataset: {row['dataset']}")
                print(f"    Camera: {row['cam']}")
                print()

Citation

If you use OmniFall in your research, please cite our paper (will be updated soon) as well as all sub-dataset papers:

@misc{omnifall,
      title={OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection}, 
      author={David Schneider and Zdravko Marinov and Rafael Baur and Zeyun Zhong and Rodi Düger and Rainer Stiefelhagen},
      year={2025},
      eprint={2505.19889},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.19889}, 
},

@inproceedings{omnifall_cmdfall,
  title={A multi-modal multi-view dataset for human fall analysis and preliminary investigation on modality},
  author={Tran, Thanh-Hai and Le, Thi-Lan and Pham, Dinh-Tan and Hoang, Van-Nam and Khong, Van-Minh and Tran, Quoc-Toan and Nguyen, Thai-Son and Pham, Cuong},
  booktitle={2018 24th International Conference on Pattern Recognition (ICPR)},
  pages={1947--1952},
  year={2018},
  organization={IEEE}
},

@article{omnifall_up-fall,
  title={UP-fall detection dataset: A multimodal approach},
  author={Mart{\'\i}nez-Villase{\~n}or, Lourdes and Ponce, Hiram and Brieva, Jorge and Moya-Albor, Ernesto and N{\'u}{\~n}ez-Mart{\'\i}nez, Jos{\'e} and Pe{\~n}afort-Asturiano, Carlos},
  journal={Sensors},
  volume={19},
  number={9},
  pages={1988},
  year={2019},
  publisher={MDPI}
},

@article{omnifall_le2i,
  title={Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification},
  author={Charfi, Imen and Miteran, Johel and Dubois, Julien and Atri, Mohamed and Tourki, Rached},
  journal={Journal of Electronic Imaging},
  volume={22},
  number={4},
  pages={041106--041106},
  year={2013},
  publisher={Society of Photo-Optical Instrumentation Engineers}
},

@article{omnifall_gmdcsa,
  title={GMDCSA-24: A dataset for human fall detection in videos},
  author={Alam, Ekram and Sufian, Abu and Dutta, Paramartha and Leo, Marco and Hameed, Ibrahim A},
  journal={Data in Brief},
  volume={57},
  pages={110892},
  year={2024},
  publisher={Elsevier}
},

@article{omnifall_cauca,
  title={Dataset CAUCAFall},
  author={Eraso, Jose Camilo and Mu{\~n}oz, Elena and Mu{\~n}oz, Mariela and Pinto, Jesus},
  journal={Mendeley Data},
  volume={4},
  year={2022}
},

@inproceedings{omnifall_edf_occu,
  title={Evaluating depth-based computer vision methods for fall detection under occlusions},
  author={Zhang, Zhong and Conly, Christopher and Athitsos, Vassilis},
  booktitle={International symposium on visual computing},
  pages={196--207},
  year={2014},
  organization={Springer}
},

@article{omnifall_mcfd,
  title={Multiple cameras fall dataset},
  author={Auvinet, Edouard and Rougier, Caroline and Meunier, Jean and St-Arnaud, Alain and Rousseau, Jacqueline},
  journal={DIRO-Universit{\'e} de Montr{\'e}al, Tech. Rep},
  volume={1350},
  pages={24},
  year={2010}
},

@inproceedings{omnifall_oops,
  title={Oops! predicting unintentional action in video},
  author={Epstein, Dave and Chen, Boyuan and Vondrick, Carl},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={919--929},
  year={2020}
}

License

The annotations and split definitions in this repository are released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The original video data belongs to their respective owners and should be obtained from the original sources.

Contact

For questions about the dataset, please contact [david.schneider@kit.edu].