Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms
To address these limitations, we propose EVAD, an event enhanced VAD framework that jointly exploits conventional video and event streams captured by bio inspired event cameras. Event sensors asynchronously capture brightness changes with high temporal resolution, offering robustness to motion blur and extreme lighting, and providing motion salient cues complementary to video based visual information. To support multi modal VAD research, we construct a large scale visible event benchmark comprising 6.3 billion events and 376,368 video frames collected under diverse illumination levels, motion patterns, and background complexities, filling the gap of realistic and scalable datasets for event based anomaly detection.
- ▪To address these limitations, we propose EVAD, an event enhanced VAD framework that jointly exploits conventional video and event streams captured by bio inspired event cameras.
- ▪Event sensors asynchronously capture brightness changes with high temporal resolution, offering robustness to motion blur and extreme lighting, and providing motion salient cues complementary to video based visual information.
- ▪To support multi modal VAD research, we construct a large scale visible event benchmark comprising 6.3 billion events and 376,368 video frames collected under diverse illumination levels, motion patterns, and background complexities, fillin
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Computer Science > Computer Vision and Pattern Recognition arXiv:2607.09114 (cs) [Submitted on 10 Jul 2026] Title:Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms Authors:Peipei Zhu, Yueqing Niu, Lin Zhu, Guanchong Niu, Yang Yu, Zheng Li View a PDF of the paper titled Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms, by Peipei Zhu and 5 other authors View PDF HTML (experimental) Abstract:Video anomaly detection (VAD) is critical for automated surveillance but remains fragile under challenging conditions such as illumination variations, fast motion, and complex backgrounds when relying solely on visible light videos.
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