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Video anomaly detection is an essential but challenging task. The prevalentmethods mainly investigate the reconstruction difference between normal andabnormal patterns but ignore the semantics consistency between appearance andmotion information of behavior patterns, making the results highly dependent onthe local context of frame sequences and lacking the understanding of behaviorsemantics. To address this issue, we propose a framework of Appearance-MotionSemantics Representation Consistency that uses the gap of appearance and motionsemantic representation consistency between normal and abnormal data. Thetwo-stream structure is designed to encode the appearance and motioninformation representation of normal samples, and a novel consistency loss isproposed to enhance the consistency of feature semantics so that anomalies withlow consistency can be identified. Moreover, the lower consistency features ofanomalies can be used to deteriorate the quality of the predicted frame, whichmakes anomalies easier to spot. Experimental results demonstrate theeffectiveness of the proposed method.

Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency