Multi-scale Spatial-temporal Interaction Network for Video Anomaly Detection

Video anomaly detection (VAD) is an essential yet challenge task in signal processing. Since certain anomalies cannot be detected by analyzing temporal or spatial information alone, the interaction between two types of information is considered crucial for VAD. However, current dual-stream architectures either limit interaction between the two types of information to the bottleneck of autoencoder or incorporate background pixels irrelevant to anomalies into the interaction. To this end, we propose a multi-scale spatial-temporal interaction network (MSTI-Net) for VAD. First, to pay particular attention to objects and reconcile the significant semantic differences between the two information, we propose an attention-based spatial-temporal fusion module (ASTM) as a substitute for the conventional direct fusion. Furthermore, we inject multi ASTM-based connections between the appearance and motion pathways of a dual stream network to facilitate spatial-temporal interaction at all possible scales. Finally, the regular information learned from multiple scales is recorded in memory to enhance the differentiation between anomalies and normal events during the testing phase. Solid experimental results on three standard datasets validate the effectiveness of our approach, which achieve AUCs of 96.8% for UCSD Ped2, 87.6% for CUHK Avenue, and 73.9% for the ShanghaiTech dataset.