Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms

2026-07-10 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

EVAD is an event-enhanced Video Anomaly Detection (VAD) framework designed to improve robustness in challenging surveillance conditions like varying illumination and complex backgrounds. It jointly exploits conventional video and event streams from bio-inspired event cameras, which capture brightness changes asynchronously with high temporal resolution, offering resilience to motion blur and extreme lighting. To support this multi-modal VAD research, a large-scale visible-event benchmark dataset was constructed, comprising 6.3 billion events and 376,368 video frames. EVAD employs a contrastive multi-modal pretraining framework to learn discriminative event representations by aligning semantic embeddings across event streams, visible videos, and textual descriptions, further integrating these with video-based spatial semantics via an adaptive fusion module. Experiments on benchmarks and the TJUTCM Pha dataset validate EVAD's superior performance.

Key takeaway

For Computer Vision Engineers designing robust surveillance systems, integrating event cameras with conventional video streams is crucial. You should consider EVAD's multi-modal approach to overcome limitations like illumination variations and motion blur, utilizing event sensors' high temporal resolution and motion-salient cues. This method demonstrably enhances anomaly detection performance in real-world scenarios, suggesting a shift towards multi-sensor fusion for improved system resilience.

Key insights

Event cameras enhance video anomaly detection by providing robust, motion-salient cues complementary to visible light.

Principles

• Event sensors offer robustness to motion blur.
• Event streams provide motion-salient cues.
• Multi-modal fusion improves VAD resilience.

Method

A contrastive multi-modal pretraining framework learns event representations by aligning semantic embeddings across event streams, visible videos, and textual descriptions, followed by adaptive fusion.

In practice

• Integrate event cameras for robust VAD.
• Utilize multi-modal pretraining for VAD.
• Develop adaptive fusion for diverse data.

Topics

• Video Anomaly Detection
• Event Cameras
• Multi-Modal Fusion
• Contrastive Learning
• Surveillance Systems
• Benchmark Datasets

Best for: AI Scientist, Computer Vision Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.