Towards Video Anomaly Detection from Event Streams: A Baseline and Benchmark Datasets

Event-based vision, characterized by low redundancy, focuson dynamic motion, and inherent privacy-preserving properties, natu-rally fits the demands of video anomaly detection (VAD). However, theabsence of dedicated event-stream anomaly detection datasets and effec-tive modeling strategies has significantly hindered progress in this field.In this work, we take the first major step toward establishing event-based VAD as a unified research direction. We first construct multi-ple simulated event-stream based benchmarks for video anomaly de-tection, featuring synchronized event and RGB recordings. Leveragingthe unique properties of events, we then propose an EVent-centric spa-tiotemporal Video Anomaly Detection framework, namely EWAD, withthree key innovations: an event density aware dynamic sampling strategyto select temporally informative segments; a density-modulated tempo-ral modeling approach that captures contextual relations from sparseevent streams; and an RGB-to-event knowledge distillation mechanismto enhance event-based representations under weak supervision. Exten-sive experiments on three benchmarks demonstrate that our EWADachieves significant improvements over existing approaches, highlight-ing the potential and effectiveness of event-driven modeling for videoanomaly detection. The benchmark datasets and code are available athttps://github.com/kanyutingfeng/EWAD.