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

• URL: http://arxiv.org/abs/2505.19889v1
• Date: Mon, 26 May 2025 12:19:11 GMT
• Title: OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection
• Authors: David Schneider, Zdravko Marinov, Rafael Baur, Zeyun Zhong, Rodi Düger, Rainer Stiefelhagen,
• Abstract summary: We introduce OmniFall, unifying eight public fall detection datasets.<n>For real-world evaluation we curate OOPS-Fall from genuine accident videos.<n>Experiments with frozen pre-trained backbones such as I3D or VideoMAE reveal significant performance gaps between in-distribution and in-the-wild scenarios.
• Score: 23.863250825189795
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Current video-based fall detection research mostly relies on small, staged datasets with significant domain biases concerning background, lighting, and camera setup resulting in unknown real-world performance. We introduce OmniFall, unifying eight public fall detection datasets (roughly 14 h of recordings, roughly 42 h of multiview data, 101 subjects, 29 camera views) under a consistent ten-class taxonomy with standardized evaluation protocols. Our benchmark provides complete video segmentation labels and enables fair cross-dataset comparison previously impossible with incompatible annotation schemes. For real-world evaluation we curate OOPS-Fall from genuine accident videos and establish a staged-to-wild protocol measuring generalization from controlled to uncontrolled environments. Experiments with frozen pre-trained backbones such as I3D or VideoMAE reveal significant performance gaps between in-distribution and in-the-wild scenarios, highlighting critical challenges in developing robust fall detection systems. OmniFall Dataset at https://huggingface.co/datasets/simplexsigil2/omnifall , Code at https://github.com/simplexsigil/omnifall-experiments

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