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

• URL: http://arxiv.org/abs/2204.04151v1
• Date: Fri, 8 Apr 2022 15:59:57 GMT
• Title: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency
• Authors: Xiangyu Huang, Caidan Zhao, Yilin Wang, Zhiqiang Wu
• Abstract summary: We propose a framework that uses normal data's appearance and motion semantic representation consistency to handle anomaly detection. We design a two-stream encoder to encode the appearance and motion information representations of normal samples. Lower consistency of appearance and motion features of anomalous samples can be used to generate predicted frames with larger reconstruction error.
• Score: 18.06814233420315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video anomaly detection refers to the identification of events that deviate from the expected behavior. Due to the lack of anomalous samples in training, video anomaly detection becomes a very challenging task. Existing methods almost follow a reconstruction or future frame prediction mode. However, these methods ignore the consistency between appearance and motion information of samples, which limits their anomaly detection performance. Anomalies only occur in the moving foreground of surveillance videos, so the semantics expressed by video frame sequences and optical flow without background information in anomaly detection should be highly consistent and significant for anomaly detection. Based on this idea, we propose Appearance-Motion Semantics Representation Consistency (AMSRC), a framework that uses normal data's appearance and motion semantic representation consistency to handle anomaly detection. Firstly, we design a two-stream encoder to encode the appearance and motion information representations of normal samples and introduce constraints to further enhance the consistency of the feature semantics between appearance and motion information of normal samples so that abnormal samples with low consistency appearance and motion feature representation can be identified. Moreover, the lower consistency of appearance and motion features of anomalous samples can be used to generate predicted frames with larger reconstruction error, which makes anomalies easier to spot. Experimental results demonstrate the effectiveness of the proposed method.

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