Spatial–Temporal Decoupled Invertible Neural Networks for Skeleton-Based Video Anomaly Detection

This research proposes STD-INN (Spatial-Temporal Decoupled Invertible Neural Networks), a new AI method for detecting anomalies in skeleton-based video, which works by separating body pose information from motion patterns rather than processing them together. The approach uses normalizing flows (a type of neural network architecture) to analyze these two components independently: spatial flows examine unusual body positions using skeletal structure, while temporal flows detect irregular movement patterns using sequential analysis. The method achieves better performance than existing approaches while using fewer parameters and providing clearer explanations of why anomalies are detected.