Human Behavior Recognition Method Based on Double-Branch Deep Convolution Neural Network
作者单位：School of AutomationGuangdong University of Technology Guangdong Institute of Intelligent Manufacturing School of information Science and Engineering Shenyang University of Technology
会议名称：《第30届中国控制与决策会议》
会议日期：2018年
学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程]
关 键 词：2D image deep convolution neural network behavior recognition human skeleton multi-classification support vector machines
摘 要：Aiming at the poor robustness and the low accuracy of 2 D image recognition, this paper presents a method of human behaviors recognition based on double-branch deep convolution neural network. Firstly, the features of the input image are extracted, and the feature maps are input into the double-branch deep convolution neural network to obtain the joints information of human body and the joints connection information of human body respectively. And we used the Hopcroft-Karp algorithm to optimally match skeletal joints to obtain the human skeletal sequence diagram. The human behaviors are identified by the multi-classification support vector machines. Finally, through the training and testing of standing, walking, running, waving, bending, squatting and sitting on seven kinds of general human behaviors, the experimental results show that the proposed approach has good accuracy and robustness.