OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust Fall Detection

Abstract

Visual fall detection models are usually trained on small,staged datasets. Their real-world utility remains unclear; such data lacksdiversity and evaluation protocols differ from paper to paper. We proposeOmniFall, a unified benchmark of 15k videos (80 hours) with frame-levelannotations in a single 16-class taxonomy. It spans three domains: OF-Staged unifies eight staged datasets with cross-subject and cross-viewsplits; OF-Synthetic adds 12k videos (17 h) with controlled demographicand environmental diversity; and OF-In-the-Wild provides a test-onlyset of genuine accident videos. We evaluate fine-tuned models as wellas much larger zero-shot multimodal LLMs. On in-the-wild fall events,both do comparably well. The clinically critical fallen state is wherethey part: zero-shot models keep confusing fallen with lying, whereasmodels fine-tuned on synthetic data with explicit fallen-state scenes dosubstantially better. We release the unified annotations, the syntheticdata, and the in-the-wild test set to foster the development of fall andfallen-state detectors for uncontrolled environments.Dataset: https://hf.co/datasets/simplexsigil2/omnifall