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

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.