基于频域注意力时空卷积网络的步态识别方法  

Gait recognition method based on frequency domain attention spatio-temporal convolutional network

作　　者：赵国顺 方建安[1,2] 瞿斌杰 Samah AFManssor 孙韶媛 Zhao Guoshun;Fang Jianan;Qu Binjie;Samah A.F.Manssor;Sun Shaoyuan(School of Information Science and Technology,Donghua University,Shanghai 201620,China;Engineering Research Center of Digitized Textile&Apparel Technology,Ministry of Education,Shanghai 201620,China)

机构地区：[1]东华大学信息科学与技术学院,上海201620 [2]数字化纺织服装技术教育部工程研究中心,上海201620

出　　处：《信息技术与网络安全》2020年第6期13-18,共6页Information Technology and Network Security

摘　　要：为了解决步态信息冗余多、特征重要性分布不均匀以及步态的时空特征难以学习的问题,提出了基于频域注意力的时空卷积网络进行步态识别。该方法改进了三维卷积网络(C3D)学习时空特征,同时提出了一种频域注意力卷积操作,既减少了冗余计算,注意力的调整又提高了学习效果。网络首先将步态信息划分为五组,然后通过改进的卷积进行时空特征抽取,最后通过Softmax层进行分类。在中科大数据集CASIA dataset B中进行测试,在Bag状态与Coat状态下准确率分别为88.5%、92.8%,分别较传统深度卷积网络(Deep CNN)提升3%左右,同时注意力在网络学习中重新分布,各个角度下的准确率也平均提升2%左右。In order to solve the problems of redundant gait information, uneven distribution of feature importance, and difficulty in learning the spatiotemporal features of the gait, a spatiotemporal convolutional network based on attention in the frequency domain was proposed for gait recognition. In the experiment, the spatial and temporal characteristics of three-dimensional convolutional network (C3D) learning were improved. At the same time, a frequency-domain attention convolution operation was proposed, which not only reduced redundant calculations, but also adjusted the attention and improved the learning effect. The network firstly divides the gait information into five groups, then extracts the spa-tiotemporal features through improved convolution, and finally classifies them through the Softmax layer. Tested in the CASIA dataset B of the Chinese University of Science and Technology, the accuracy rates in the Bag state and Coat state are 88. 5 % and 92. 8 % respectively, which are about 3 % higher than traditional deep convolutional networks (Deep CNN). At the same time, attention is redistributed in network learning, the accuracy rate of each angle is increased by about 2 % on average.

关 键 词：频域 注意力 三维卷积 步态识别 生物特征 深度学习 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]