Video anomaly detection based on memory-guided dual-streamspatio-temporal coding network
Video anomaly detection has attracted much attention due to the unpredictability of surveillance video anomaly events and the complexity of the anomaly environment.Currently,video anomaly detection often utilises unsupervised methods to acquire video information,but the lack of spatio-temporal information acquisition during feature extraction leads to the problem of inconsistent spatio-temporal features.To this end,a memory-guided two-stream spatio-temporal coding network(MSTAE)model based on memory-guidance is proposed,and a two-stream spatio-temporal feature extraction network is designed to obtain the motion features of the video in spatial streams and the temporal features in temporal streams using continuous video frame sequences and optical flow graphs as inputs,respectively,and at the same time,the attention mechanism is introduced to improve the encoder and reduce the risky error due to the data redundancy.Extensive experiments are conducted on three open standard datasets(Ped2,Avenue and ShanghaiTech datasets),and the experimental results show that the AUC accuracy of the model outperforms the state-of-the-7art methods.
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