Prototype-guided and dynamic-aware video anomaly detection.

Abstract

Anomaly detection in intelligent surveillance system is an important and challenging task, which commonly learns a model describing normal patterns via frame reconstruction or prediction and assumes that anomalies deviate form the learned normal model. Despite deep neural networks (DNNs) enable remarkable gains on anomaly detection, most previous works suffer from following drawbacks: (1) the powerful generalization ability of DNNs allows to reconstruct or predict anomalies well, (2) the temporal contextual information is under-explored, and (3) the diversity of normal patterns is overlooked. To address above issues, we propose a prototype-guided and dynamic-aware long-distance frame prediction paradigm for video anomaly detection. Specifically, a prototype-guided dynamics matching network (PDM-Net) is adopted to lessen the generalization ability of model to anomalies. To explore the temporal contexts, PDM-Net is equipped with a dynamic prototype matching mechanism, which first stores long-term dynamic prototypes learned from normal long sequences and then recalls the stored normal long-term prototypes with dynamics extracted from short sequences for facilitating the long-distance frames prediction of normal short sequences. Besides, a feature discrimination module is adopted to extract the representative dynamic features of various normal events meanwhile preserving the diversity of normal patterns. Experimental results on four datasets demonstrate the superiority of our method.