Hierarchical Graph Embedded Pose Regularity Learning via Spatio-Temporal Transformer for Abnormal Behavior Detection

Abnormal behavior detection in surveillance video is a fundamental task in modern public security. Different from typical pixel-based solutions, pose-based approaches leverage low-dimensional and strongly-structured skeleton feature, which enables the anomaly detector to be immune to complex background noise and obtain higher efficiency. However, existing pose-based methods only utilize the pose of each individual independently while ignore the important interactions between individuals. In this paper, we present a hierarchical graph embedded pose regularity learning framework via spatio-temporal transformer, which leverages the strength of graph representation in encoding strongly-structured skeleton feature. Specifically, skeleton feature is encoded as the hierarchical graph representation, which jointly models the interactions among multiple individuals and the correlations among body joints within the same individual. Furthermore, a novel task-specific spatial-temporal graph transformer is designed to encode the hierarchical spatio-temporal graph embeddings of human skeletons and learn the regular patterns within normal training videos. Experimental results indicate that our method obtains superior performance over state-of-the-art methods on several challenging datasets.