OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust Fall Detection
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arXiv
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| Format: | Preprint |
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2025
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| author | Schneider, David Marinov, Zdravko Zhong, Zeyun Jaus, Alexander Düger, Rodi Baur, Rafael Sarfraz, M. Saquib Stiefelhagen, Rainer |
| author_facet | Schneider, David Marinov, Zdravko Zhong, Zeyun Jaus, Alexander Düger, Rodi Baur, Rafael Sarfraz, M. Saquib Stiefelhagen, Rainer |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19889 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust Fall Detection Schneider, David Marinov, Zdravko Zhong, Zeyun Jaus, Alexander Düger, Rodi Baur, Rafael Sarfraz, M. Saquib Stiefelhagen, Rainer Computer Vision and Pattern Recognition I.2.10; I.5.4 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/ |
| title | OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust Fall Detection |
| topic | Computer Vision and Pattern Recognition I.2.10; I.5.4 |
| url | https://arxiv.org/abs/2505.19889 |