基于深度时空卷积神经网络的人群异常行为检测和定位

o handle the issues of low accuracy and lacking training samples in abnormal crowd behavior detection in public places, this paper proposes a method based on deep spatial-temporal convolutional neural networks in this paper. In view of the characteristics of crowd behavior in monitoring videos, a deep spatial-temporal convolution neural network for detecting abnormal crowd behavior is first designed by extending 2D convolution to the 3D space according to spatial features of static images and temporal features between the frames before and after the current frame. To locating abnormal crowd, this paper divides video frames into a number of subregions that obtain spatial-temporal samples. Then, the samples are input into the designed deep spatial-temporal convolutional neural network for training and classification, whose results are used to detect and locate abnormal crowd. In the meanwhile, this paper utilizes a transfer learning method to deal with the issue of lacking training samples when training the deep spatial-temporal convolutional neural network, where datasets with more training samples are used to pre-train the network which is fine-tuned and optimized on testing datasets with fewer samples. Experimental results show that the detection accuracies on UCSD and Subway open datasets are greater than 99% and 93%, respectively.