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

• URL: http://arxiv.org/abs/2603.24991v1
• Date: Thu, 26 Mar 2026 03:33:33 GMT
• Title: Towards Video Anomaly Detection from Event Streams: A Baseline and Benchmark Datasets
• Abstract summary: We take the first major step toward establishing event-based anomaly detection as a unified research direction.<n>We first construct multiple eventstream based benchmarks for video anomaly detection, featuring synchronized event and RGB recordings.<n>We then propose an EVent-centric Video Anomaly Detection framework, namely EWAD, with three key innovations.
• Score: 71.53287557600177
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event-based vision, characterized by low redundancy, focus on dynamic motion, and inherent privacy-preserving properties, naturally fits the demands of video anomaly detection (VAD). However, the absence of dedicated event-stream anomaly detection datasets and effective 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 multiple event-stream based benchmarks for video anomaly detection, featuring synchronized event and RGB recordings. Leveraging the unique properties of events, we then propose an EVent-centric spatiotemporal Video Anomaly Detection framework, namely EWAD, with three key innovations: an event density aware dynamic sampling strategy to select temporally informative segments; a density-modulated temporal modeling approach that captures contextual relations from sparse event streams; and an RGB-to-event knowledge distillation mechanism to enhance event-based representations under weak supervision. Extensive experiments on three benchmarks demonstrate that our EWAD achieves significant improvements over existing approaches, highlighting the potential and effectiveness of event-driven modeling for video anomaly detection. The benchmark datasets will be made publicly available.

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