Video anomaly detection (VAD) is critical for automated surveillance but remains fragile under challenging conditions such as illumination variations, fast motion, and complex backgrounds. 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, 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. This fills the gap of realistic and scalable datasets for event-based anomaly detection.
Building upon this dataset, we design a contrastive multi-modal pretraining framework to learn discriminative event representations by aligning semantic embeddings across event streams, visible videos, and textual descriptions. An adaptive fusion module dynamically integrates event-based temporal cues with video-based spatial semantics, improving robustness to environmental disturbances.
Experiments on benchmarks and the proposed TJUTCM Pha dataset demonstrate that EVAD consistently outperforms existing methods, validating the effectiveness of event-based sensing for VAD in real-world scenarios.
Blogger's Review: The EVAD framework showcases immense potential for video anomaly detection in complex environments by merging traditional video with event streams. This innovation not only enhances detection accuracy but also opens new avenues for multi-modal data research.