一种基于人体轮廓形变场的双流网络步态识别方法  

A Dual-stream Network Based on Body Contour Deformation Field for Gait Recognition

作　　者：霍威 王科 唐俊[1] 王年[1] 梁栋 HUO Wei;WANG Ke;TANG Jun;WANG Nian;LIANG Dong(School of Electronic and Information Engineering,Anhui University,Hefei 230601,China;School of Internet,Anhui University,Hefei 230039,China)

机构地区：[1]安徽大学电子信息工程学院,合肥230601 [2]安徽大学互联网学院,合肥230039

出　　处：《电子与信息学报》2024年第10期4062-4071,共10页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62273001,61772032);安徽省重点研究与开发计划(2022k07020006);安徽高校自然科学研究重大项目(KJ2021ZD0004)。

摘　　要：步态识别易受相机视角、服装和携带物等外界因素影响而性能下降。为此,该文将非刚性点集配准引入步态识别,利用相邻步态帧之间的形变场表征行走过程中人体轮廓发生的位移量,从而提升对人体形态变化的动态感知能力。在此基础上,该文提出一种基于人体轮廓形变场的双流卷积神经网络GaitDef,该网络模型由形变场和步态剪影两路特征提取分支构成。针对形变场数据的稀疏性设计多尺度特征提取模块,以获取形变场的多层次空间结构信息。针对步态剪影提出动态差异捕捉模块和上下文信息增强模块,以捕捉动态区域的变化特性和利用上下文信息增强步态表征能力。双分支网络的输出特征经过特征融合得到最终的步态表示。大量实验结果表明了该文方法的有效性,在CASIA-B和CCPG数据集上,该文方法的平均Rank-1准确率分别能达到93.5%和68.3%。Gait recognition is susceptible to external factors such as camera viewpoints,clothing,and carrying conditions,which could lead to performance degradation.To address these issues,the technique of non-rigid point set registration is introduced into gait recognition,which is used to improve the dynamic perception ability of human morphological changes by utilizing the deformation field between adjacent gait frames to represent the displacement of human contours during walking.Accordingly,a dual-flow convolutional neural network-GaitDef exploiting human contour deformation field is proposed in this paper,which consists of deformation field and gait silhouette extraction branches.Besides,a multi-scale feature extraction module is designed for the sparsity of deformation field data to obtain multi-level spatial structure information of the deformation field.A dynamic difference capture module and a context information augmentation module are proposed to capture the changing characteristics of dynamic regions in gait silhouettes and consequently enhance gait representation ability by utilizing context information.The output features of the dual-branch network structure are fused to obtain the final gait representation.Extensive experimental results verify the effectiveness of GaitDef.The average Rank-1 accuracy of GaitDef can achieve 93.5%和68.3%on CASIA-B and CCPG datasets,respectively.

关 键 词：步态识别 点集配准 卷积神经网络 特征融合 

分 类 号：TN911.73[电子电信—通信与信息系统] TP391.41[电子电信—信息与通信工程]