Weakly Supervised Video Anomaly Detection Based on Dual Dynamic Memory Network
Video anomaly detection aims to identify frame-level abnormal behaviors from the video.The weakly supervised me-thods use both normal and abnormal video supplemented by the video-level labels for training,which show better performance than the unsupervised methods.However,the current weakly supervised video anomaly detection methods cannot record the long-term mode of the video.At the same time,some methods use the information of future frames to achieve better detection results,which makes it impossible to apply online.For this reason,a weakly supervised video anomaly detection method based on dual dy-namic memory network is proposed for the first time in this paper.The memory network containing two memory modules is de-signed to record the normal and abnormal modes of video in the long term respectively.In order to realize the collaborative update of video features and memory items,the read operation is used to enhance the features of video frames based on the memory items in the memory module,and the write operation is used to update the contents of memory items based on the features of video frames.At the same time,the number of memory items will be dynamically adjusted during the training process to meet the needs of different video monitoring scenarios.In training,a modality separation loss is proposed to increase the discrimination between memory items.During the test,only memory items are needed without the participation of future video frames,so that accurate online detection can be achieved.Experimental results on two public weakly supervised video anomaly detection datasets show that the proposed method is superior to all online application methods,and also has strong competitiveness compared with offline application methods.
Video anomaly detectionWeakly supervised learningMemory networkMultiple instance learningDeep learning
NETL
NSTL
万方数据
