A video anomaly detection framework based on multi-scale dynamic prototype unit

Many existing works fail to make full use of temporal information and ignore the diversity of normal behaviors in video anomaly detection tasks. In this paper, we propose a multi-scale dynamic prototype unit based video anomaly detection method. Some works proposed an autoencoder anomaly detection model based on dynamic prototype unit (DPU), which effectively improves the performance of anomaly detection, but ignores the importance of different levels of features for normal event modeling. Therefore, this paper proposes an anomaly detection model based on multi-scale dynamic prototype unit (DPU), which uses memory units to establish connections between encoder and decoder. Normal patterns at different scales are learned. In addition, based on the Temporal Shift technique, the temporal information of video can be mined more effectively to generate future video frames. Experimental results on UCSD Ped2, CUHK Avenue and ShanghaiTech datasets show that the proposed method is superior to the current mainstream video anomaly detection methods while meeting the real-time requirements.