关键词: 多示例学习, 弱监督学习, 时序卷积网络, 异常事件检测, 注意力机制

Abstract: Weakly supervised anomaly detection (WSAD) is a challenging task in that there is only normal and anomaly video label supervision but it is required to localize intervals where anomalies take place.We employ multiple instance learning (MIL) network for weakly supervised anomaly detection,which regards the input video as a bag and the segments chunked from the vi-deo as instances in it.We train the instance classifier with only label of video level (bag level),while the label of instance level is unknown.As there is strong temporal information in videos,we focus on temporal relationship for online anomaly detection in surveillance videos.We consider both global and local perspective and use self-attention module to learn each instance weight.We get the linear weighted sum of self-attention score and instance anomaly score,which represents video level anomaly score.Then the mean square error loss is employed to train the self-attention module.Online constraints allow us to use historical and current video clips only,without future frames.In order to model the temporal structure of video,we introduce LSTM and temporal con-volutional network (TCN) into WSAD problem.We explore single rate dilated temporal convolutional network,and pyramid dilated temporal convolutional network (PDTCN) which fuses multi-scale feature with different rates.Experiments show that the AUC of PDTCN with complementary inner and outer bag loss is higher than that of the baseline method without temporal mode-ling by 3.2% on UCF-Crime dataset.

Key words: Anomaly detection, Attention module, Multiple instance learning, Temporal convolutional network, Weakly-supervised learning