A Two-Stream Information Fusion Approach to Abnormal Event Detection in Video.

Human abnormal activity detection for automatic surveillance systems is to detect abnormal objects and human behaviours in videos. In this paper, we propose to explicitly address different kinds of abnormal events by developing a two-stream fusion approach that integrates both geometry and image texture information. To be concrete, we firstly propose to utilize an object detector to divide the abnormal events into two catalogues: abnormal human behaviors and abnormal objects. For the detection of abnormal human behaviours, we exploit a spatial-temporal graph convolutional network (ST-GCN) which considers both spatial and temporal domains to capture the geometrical features from human pose graphs. The extracted geometric feature embeddings are further adapted with a clustering step to cluster the temporal graphs and output normality scores. For the detection of abnormal objects, the obtained from the object detector are reused to assist with generating normality scores of possible anomalies. Finally, a late fusion is performed to integrate normality scores from both screams for final decision. The experimental results on the datasets of UCSD PED2 and ShanghaiTech Campus demonstrate the effectiveness of our proposed approach and the improved performance compared to other state-of-the-art approaches.