Hierarchical activity discovery within spatio-temporal context for video anomaly detection

In this paper, we present a novel approach for video anomaly detection in crowded and complicated scenes. The proposed approach detects anomalies based on a hierarchical activity pattern discovery framework comprehensively considering both global and local spatio-temporal contexts. The discovery is a coarse-to-fine learning process with unsupervised ways for automatically constructing normal activity patterns at different levels. An unified anomaly energy function is designed based on these discovered activity patterns to identify the abnormal level of an input motion pattern. We demonstrate the efficiency of the proposed method on the UCSD anomaly detection datasets (Ped1 and Ped2) and compare the performance with existing work.