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

OmniFall is a comprehensive fall detection benchmark with dense temporal segment annotations across three components: OF-Staged (8 public lab datasets), OF-In-the-Wild (genuine accidents from OOPS), and OF-Synthetic (12,000 diffusion-generated videos with demographic diversity). All components share a sixteen-class activity taxonomy.

[Paper] [Project Page]

Quickstart

Labels only (no video files needed)

from datasets import load_dataset

# 8 staged datasets, cross-subject split
ds = load_dataset("simplexsigil2/omnifall", "of-sta-cs")
print(ds["train"][0])  # {'path': ..., 'label': 1, 'start': 0.0, 'end': 2.5, ...}

# Cross-domain: train on staged, test on all (staged + itw + syn)
ds = load_dataset("simplexsigil2/omnifall", "of-sta-to-all-cs")

# Synthetic data with demographic metadata (19 columns)
ds = load_dataset("simplexsigil2/omnifall", "of-syn")

With video loading (pip install omnifall)

import omnifall

# OF-Syn videos auto-download from HF Hub (~9.1GB, cached)
ds = omnifall.load("of-syn", video=True)

# OF-ItW requires one-time OOPS video preparation
omnifall.prepare_oops()  # streams ~45GB, extracts ~2.6GB
ds = omnifall.load("of-itw", video=True)

# The video column contains absolute file paths (strings)
print(ds["train"][0]["video"])

Video paths can also be added to an already-loaded dataset via omnifall.add_video(ds, config="of-syn"). OOPS preparation is also available via CLI: omnifall prepare-oops.

Overview

	Videos	Segments (SV)	Duration (SV)
OF-Staged (8 datasets)	2,164	9,590	13.81h
OF-ItW (OOPS)	818	4,022	2.65h
OF-Syn	12,000	19,228	16.88h
Total	14,982	32,840	33.34h

Dataset	Type	Videos	Segments (SV)	Duration (SV)	Avg Seg (s)
CMDFall	multi (7 views)	384	6,026	7.12h	4.25
UP-Fall	multi (2 views)	1,118	1,213	4.59h	13.63
Le2i	single	190	967	0.79h	2.95
GMDCSA24	single	160	458	0.36h	2.80
CAUCAFall	single	100	258	0.28h	3.85
EDF	multi (2 views)	10	254	0.22h	3.14
OCCU	multi (2 views)	10	245	0.25h	3.54
MCFD	multi (8 views)	192	169	0.20h	4.26
OOPS-Fall	single	818	4,022	2.65h	2.38
OF-Syn	single	12,000	19,228	16.88h	3.16

SV = single-view (one count per unique camera perspective). Multi-view datasets have additional synchronized views; see statistics.md for full multi-view counts and class distributions.

Configs

Over 70 configurations are available. Each returns train/validation/test splits.

Category	Examples	Description
Same-domain	of-sta-cs, of-sta-cv, of-itw, of-syn	Train and test from the same source
Cross-domain (to-all)	of-sta-to-all-cs, of-syn-to-all-cs	Train on one source, test on all (staged + ItW + Syn)
Individual to-all	cmdfall-to-all-cs, edf-to-all-cv	Train on single dataset, test on all
OF-Syn demographic	of-syn-cross-age, of-syn-cross-bmi	Cross-demographic generalization splits
Aggregate	cs, cv	All staged + OOPS combined
Individual	cmdfall-cs, le2i-cv	Single staged dataset
Labels/metadata	labels, labels-syn, framewise-syn	Raw annotations without splits

See CONFIGS.md for the complete configuration reference including deprecated names.

Citation

If you use OmniFall in your research, please cite our paper as well as the sub-dataset papers:

@misc{omnifall,
      title={OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust 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 are released under CC BY-NC-SA 4.0. 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].

Documentation

• statistics.md - Full dataset statistics and class distributions
• CONFIGS.md - Complete configuration reference and deprecated names
• STRUCTURE.md - Repository structure and data formats
• LABELS.md - Label definitions and annotation guidelines
• omnifall_dataset_examples.ipynb - Interactive examples with video loading and visualization