Unsupervised Video Anomaly Detection Based on Graph Attention Propagation and Semantic Information

Abstract

Video Anomaly Detection (VAD) is a crucial computer vision task for security monitoring and public safety. Unsupervised VAD is more suitable for real-world scenarios with rare unknown anomalies, but existing LLM-based methods suffer from limited temporal modeling, inconsistent video understand ing and inaccurate fine-grained localization, leading to biased anomaly scoring. To solve these problems, we propose a novel unsupervised VAD framework fus ing graph attention propagation and multimodal semantic information: first, fuse video semantic and motion features to construct a dynamic spatiotemporal graph, and refine node features via graph attention propagation with orthogonal con straints; then, split videos into semantically coherent event units by a statistical boundary detection module; finally, guide MLLMs to generate event semantic descriptions and initial anomaly scores through a hierarchical prompting strategy, and refine the scores via video-text semantic alignment to obtain accurate frame level scores. Evaluated on UCF-Crime and XD-Violence datasets with frame level AUC, the proposed framework achieves state-of-the-art performance under unsupervised and zero-shot settings, significantly outperforming existing LLM based VAD methods and even several weakly supervised approaches, which fully verifies its effectiveness and robustness.