Visual fall detection models trained on small, staged datasets have unclear real-world utility due to limited diversity and inconsistent evaluation protocols. We present OmniFall, a unified benchmark with 80 hours / 15k videos and dense frame-level annotations in a harmonized 16-class taxonomy, spanning three complementary domains: OF-Staged (eight public staged sets, standardized with cross-subject/view splits), OF-Synthetic (12k videos, 17 h; controlled diversity in age, body type, environment, camera), and OF-In-the-Wild (the first test-only benchmark curated from genuine accident videos). OmniFall supports both video classification and timeline segmentation, and its cross-domain protocol isolates staged/synthetic-to-wild generalization. Our results show that carefully designed synthetic data can match or surpass real staged footage on cross-domain transfer, while reducing privacy risk and easing data collection. By combining privacy-amenable synthetic/staged sources with a public, test-only wild target and releasing dense, standardized timelines, OmniFall provides a comprehensive benchmark for privacy-preserving fall detection and fall-related (pre/post-fall) segmentation, enabling robust detectors that generalize to uncontrolled environments. Project page: http://simplexsigil.github.io/omnifall/
Cite
@article{arxiv.2505.19889,
title = {OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust Fall Detection},
author = {David Schneider and Zdravko Marinov and Zeyun Zhong and Alexander Jaus and Rodi Düger and Rafael Baur and M. Saquib Sarfraz and Rainer Stiefelhagen},
journal= {arXiv preprint arXiv:2505.19889},
year = {2026}
}