Cross-View Gait Recognition Method Based on Multi-branch Residual Deep Network
HU Shaohui1, WANG Xiuhui1, LIU Yanqiu1
1. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018
Abstract：Convolution neural network based gait recognition cannot make full use of local fine-grained information. To solve the problem, a cross-view gait recognition method based on multi-branch residual deep network is proposed. The multi-branch network is introduced into convolutional neural network to extract features with different granularity in gait contour sequences. Residual learning and multi-scale feature fusion technology are utilized to enhance the feature learning ability of the network. Experimental results on open-accessed gait datasets CASIA-B and OU-MVLP show that the recognition accuracy of the proposed method is higher than that of the existing algorithms.
胡少晖, 王修晖, 刘砚秋. 基于多支路残差深度网络的跨视角步态识别方法[J]. 模式识别与人工智能, 2021, 34(5): 455-462.
HU Shaohui, WANG Xiuhui, LIU Yanqiu. Cross-View Gait Recognition Method Based on Multi-branch Residual Deep Network. , 2021, 34(5): 455-462.
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2021, Vol. 34 
