关键词: 暴力行为检测, (2+1)D密集残差块, 残差网络, 深度学习

Abstract:

In public security,the complex violence behavior detection is of important research value.Traditional methods are mainly based on hand-crafted features,but they have a limited generalization ability.The existing deep learning network models have a better tolerance,but those kinds of methods face the challenge of obtaining a high accuracy.To solve the above problems,a novel violent behavior detection method is proposed in this paper by combining an improved R (2+1) D network and the dense connection idea.In the branch of the traditional R (2+1) D residual module,the three-quarters of the feature map is ignored due to the convolution operation with strides of 2.In this paper,the convolution operation is optimized for a pooling operation and a convolution operation with strides of 1,and the detection accuracy is increased by 2.3%.Besides,the dense connection idea is adopted into the residual module to establish the connection between different convolutional layers.The improvement could alleviate the problem of gradient dissipation during the training process,and the detection accuracy is further improved by 1.46%.Experimental results on one public dataset and one self-built dataset demonstrate the effectiveness of the proposed method for the complex violence behavior detection.

Key words: violence detection, (2+1) D dense residual block, residual network, deep learning