改进重建和预测网络的人体异常行为检测方法

Improve Human Abnormal Behavior Detection Method of Reconstruction and Prediction Network

在人体异常行为检测中,为了能够更加充分地利用动作和时空特征信息,提出了一种基于重建和预测网络的人体异常行为检测方法。该方法中的网络结构由重建子网络和视频预测子网络组成,其中重建子网络采用自编码器结构,以连续的视频帧作为输入来对下一帧进行重建;预测子网络采用基于3D卷积的编码器、解码器结构作为网络主干,通过输入一连串视频帧图片对后续视频帧进行预测。此外,为了能让重建子网络更好地关注人体行为的动作特征,采用詹森-香农散度(JSD)来计算重建帧与原始帧之间的差异,同时在预测子网络中添加时空一致性的正则化约束。UCSDped2、Avenue和ShanghaiTech三个数据集上的实验结果表明,该方法相比于其他的视频人体异常行为检测方法在AUC指标上有更好的表现,在UCSDped2、Avenue和ShanghaiTech数据集中分别达到了97.3%、91.1%和82.6%。

In the detection of human abnormal behavior,in order to make full use of action and spatio-temporal feature information,a detection method of human abnormal behavior based on reconstruction and prediction network is proposed.The network structure in this method consists of a reconstruction sub-network and a video prediction sub-network,in which the reconstruction sub-network adopts a self-encoder structure and reconstructs the next frame with continuous video frames as input.The prediction sub-network adopts the encoder and decoder structure based on 3D convolution as the backbone of the network,and predicts the subsequent video frames by inputting a series of video frame pictures.In addi-tion,in order to make the reconstructed sub-network pay more attention to the action characteristics of human behavior,Zhan Sen-Shannon divergence(JSD)is used to calculate the difference between the reconstructed frame and the original frame,and the regularization constraint of temporal and spatial consistency is added to the prediction sub-network.The experimental results on three datasets,UCSDped2,Avenue and ShanghaiTech,show that this method has better perfor-mance on AUC index than other video human abnormal behavior detection methods,and it reaches 97.3%,91.1%and 82.6%in UCSDped2,Avenue and ShanghaiTech datasets respectively.