Scene-Aware Context Reasoning for Unsupervised Abnormal Event Detection in Videos

In this paper, we propose a scene-aware context reasoning method that exploits context information from visual features for unsupervised abnormal event detection in videos, which bridges the semantic gap between visual context and the meaning of abnormal events. In particular, we build na spatio-temporal context graph to model visual context information including appearances of objects, spatio-temporal relationships among objects and scene types. The context information is encoded into the nodes and edges of the graph, and their states are iteratively updated by using multiple RNNs with message passing for context reasoning. To infer the spatio-temporal context graph in various scenes, we develop a graph-based deep Gaussian mixture model for scene clustering in an unsupervised manner. We then compute frame-level anomaly scores based on the context information to discriminate abnormal events in various scenes. Evaluations on three challenging datasets, including the UCF-Crime, Avenue, and ShanghaiTech datasets, demonstrate the effectiveness of our method.