基于视角转换的多视角步态识别方法

doi:10.19678/j.issn.1000-3428.0057899

计算机工程 ›› 2021, Vol. 47 ›› Issue (6): 210-216. doi: 10.19678/j.issn.1000-3428.0057899

基于视角转换的多视角步态识别方法

瞿斌杰^1,2, 孙韶媛^1,2, Samah A. F. Manssor^1,2, 赵国顺^1,2

1. 东华大学信息科学与技术学院, 上海 201620;
2. 东华大学数字化纺织服装技术教育部工程研究中心, 上海 201620

收稿日期:2020-03-30 修回日期:2020-05-12 发布日期:2020-05-22
作者简介:瞿斌杰(1996-),男,硕士研究生,主研方向为图像处理、计算机视觉;孙韶媛,教授、博士后;SamahA.F.Manssor,博士;赵国顺,硕士研究生。

Multi-view Gait Recognition Method Based on View Transformation

QU Binjie^1,2, SUN Shaoyuan^1,2, Samah A. F. Manssor^1,2, ZHAO Guoshun^1,2

1. College of Information Science and Technology, Donghua University, Shanghai 201620, China;
2. Engineering Research Center of Digitized Textile and Fashion Technology of Ministry of Education, Donghua University, Shanghai 201620, China

Received:2020-03-30 Revised:2020-05-12 Published:2020-05-22
Contact: 上海市科委基础研究基金（15JC1400600）。 E-mail:qubinjieit@163.com

摘要/Abstract

摘要： 针对步态识别中步态视角变化、步态数据样本量少及较少利用步态时间信息等问题，提出一种基于视角转换的步态识别方法。通过VTM-GAN网络，将不同视角下的步态能量图及含有步态时间信息的彩色步态能量图，统一映射到保留步态信息最丰富的侧视图视角，以此突破步态识别中多视角的限制，在视角转换的基础上，通过构建侧视图下的步态正负样本对来扩充用于网络训练的数据，并采用基于距离度量的时空双流卷积神经网络作为步态识别网络。在CASIA-B数据集上的实验结果表明，该方法在各状态、各角度下的平均识别准确率达到92.5%，优于3DCNN、SST-MSCI等步态识别方法。

关键词: 步态识别, 视角转换, VTM-GAN网络, 时空双流卷积神经网络, CASIA-B数据集

Abstract: The existing gait recognition methods are limited by multiple factors, including the changes of view, small gait sample size, and under-utilization of the temporal information of gaits.To address the problems and improve the gait recognition performance, this paper proposes a gait recognition method based on view transformation.Through VTM-GAN, Gait Energy Images(GEI) and Chrono Gait Images(CGI) with temporal gait information of different views are mapped to the side view that contains the most abundant gait information in order to break the limitation of views in gait recognition.On the basis of view transformation, the positive and negative sample pairs of gait data in the side view are constructed to extend the volume of network training data.The Spatial-temporal double flow convolutional neural network based on distance measurement is taken as the gait recognition network.Experimental results on the CASIA-B dataset show that the average recognition accuracy of this method in all states and views reaches 92.5%, higher than that of 3DCNN, SST-MSCI and other gait recognition methods.

Key words: gait recognition, view transformation, VTM-GAN network, spatial-temporal double flow Convolutional Neural Network(CNN), CASIA-B dataset

中图分类号:

TP391

瞿斌杰, 孙韶媛, Samah A. F. Manssor, 赵国顺. 基于视角转换的多视角步态识别方法[J]. 计算机工程, 2021, 47(6): 210-216.

QU Binjie, SUN Shaoyuan, Samah A. F. Manssor, ZHAO Guoshun. Multi-view Gait Recognition Method Based on View Transformation[J]. Computer Engineering, 2021, 47(6): 210-216.

https://www.ecice06.com/CN/Y2021/V47/I6/210

图/表 14

20210618184143

20210618184146

20210618184149

20210618184153

20210618184156

20210618184159

20210618184203

20210618184206

20210618184210

20210618184213

20210618184217

20210618184220

20210618184223

20210618184227

参考文献

[1] WANG Kejun,DING Xinnan,XING Xianglei,et al.A survey of multi-view gait recognition[J].Acta Automatica Sinica,2019,45(5):841-852.(in Chinese)王科俊,丁欣楠,邢向磊,等.多视角步态识别综述[J].自动化学报,2019,45(5):841-852.
[2] JOHNSON A Y,BOBICK A F.A multi-view method for gait recognition using static body parameters[C]//Proceedings of the 3rd International Conference on Audio-and Video-Based Biometric Person Authentication.Halmstad,Sweden:[s.n.],2001:301-311.
[3] ARIYANTO G,NIXON M S.Model-based 3D gait biometrics[C]//Proceedings of International Joint Conference on Biometrics.Washington,D.C.,USA:IEEE Press,2011:1-7.
[4] HAN J,BHANU B.Individual recognition using gait energy image[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(2):316-322.
[5] BOBICK A F,DAVIS J W.The recognition of human movement using temporal templates[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(3):257-267.
[6] LAM T H W,LEE R S T.A new representation for human gait recognition:motion silhouettes image[C]//Proceedings of International Conference on Biometrics.Hong Kong,China:[s.n.],2006:612-618.
[7] LAM T H W,CHEUNG K H,LIU J N K.Gait flow image:a silhouette-based gait representation for human identification[J].Pattern Recognition,2011,44(4):973-987.
[8] HE Yiwei,ZHANG Junping.Deep learning for gait recognition:a survey[J].Pattern Recognition and Artificial Intelligence,2018,31(5):442-452.(in Chinese)何逸炜,张军平.步态识别的深度学习:综述[J].模式识别与人工智能,2018,31(5):442-452.
[9] LIU Nini,LU Jiwen,TAN Yaping.Joint subspace learning for view-invariant gait recognition[J].IEEE Signal Processing Letters,2011,18(7):431-434.
[10] KUSAKUNNIRAN W,WU Q,ZHANG J,et al.A new view-invariant feature for cross-view gait recognition[J].IEEE Transactions on Information Forensics and Security,2013,8(10):1642-1653.
[11] MAKIHARA Y,SAGAWA R,MUKAIGAWA Y,et al.Gait recognition using a view transformation model in the frequency domain[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2006:151-163.
[12] KUSAKUNNIRAN W,WU Q,LI H D,et al.Multiple views gait recognition using view transformation model based on optimized gait energy image[C]//Proceedings of the 12th IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2009:1058-1064.
[13] GODBEHERE A B,MATSUKAWA A,GOLDBERG K.Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation[C]//Proceedings of the American Control Conference.Montreal,USA:IEEE Press,2012:4305-4312.
[14] RAFAEL C G,RICHARD E W.Digital image processing[M].Englewood Cliffs,USA:Prentice-Hall,Inc.,2007.
[15] WANG Chen,ZHANG Junping,WANG Liang,et al.Human identification using temporal information preserving gait template[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2164-2176.
[16] ZHU J Y,PARK T,ISOLA P,et al.Unpaired image-to-image translation using cycle-consistent adversarial networks[EB/OL].[2020-02-20].https://arxiv.org/abs/1703.10593.
[17] YU Shiqi,TAN Daoling,TAN Tieniu.A framework for evaluating the effect of view angle,clothing and carrying condition on gait recognition[C]//Proceedings of IEEE International Conference on Pattern Recognition.Washington D.C.,USA:IEEE,2006:441-444.
[18] WU Zifeng,HUANG Yongzhen,WANG Liang,et al.A comprehensive study on cross-view gait based human identification with deep CNNs[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(2):209-226.
[19] ZENG Wei,WANG Cong.View-invariant gait recognition via deterministic learning[J].Neurocomputing,2016,175(29):324-335.
[20] YU S Q,CHEN H F,REYES E B G,et al.GaitGAN:invariant gait feature extraction using generative adversarial networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops.Washington D.C.,USA:IEEE Press,2017:532-539.

选择文件类型/文献管理软件名称

选择包含的内容

基于视角转换的多视角步态识别方法

Multi-view Gait Recognition Method Based on View Transformation

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献

相关文章 12

编辑推荐

Metrics

本文评价

[1]	孙妍, 胡龙, 冯雪玲. 基于变换匹配层融合的双模态生物特征识别方法[J]. 计算机工程, 2023, 49(5): 269-276.
[2]	刘冠群,罗桂琼,谭平. 基于类Haar特征模板匹配的多镜头步态识别算法[J]. 计算机工程, 2017, 43(12): 231-236.
[3]	李敏,张琴,宋曰聪,刘恒. 视频中运动人体身份识别的认知物理学方法[J]. 计算机工程, 2016, 42(8): 146-152.
[4]	李一波, 卑珊珊, 刘婉竹, 刘金英. 基于行走时脚摆角的步态识别方法[J]. 计算机工程, 2012, 38(14): 132-134.
[5]	徐素莉, 张前进, 刘伟. 基于模糊主成分分析的步态识别[J]. 计算机工程, 2011, 37(3): 192-194.
[6]	李轶, 明东, 冯莉, 李文, 奕伟波, 白艳茹, 綦宏志, 万柏坤. 红外图像中基于混合特征的步态识别方法[J]. 计算机工程, 2011, 37(20): 1-3.
[7]	肖德贵, 王磊. 基于加权行质量向量的步态识别方法[J]. 计算机工程, 2011, 37(11): 215-217.
[8]	汪丹桂, 罗斌, 翟素兰. Choquet模糊积分特征融合的步态识别[J]. 计算机工程, 2010, 36(21): 170-172.
[9]	徐中宇, 姜洪霖, 张忠波. 基于改进C-V模型的步态轮廓提取方法[J]. 计算机工程, 2010, 36(17): 182-183,186.
[10]	路远. 基于模糊支持向量机的步态识别[J]. 计算机工程, 2009, 35(21): 189-191.
[11]	赵喜玲;李其申;卢致天;李俊峰. 一种改进的步态识别方法[J]. 计算机工程, 2009, 35(2): 177-179.
[12]	薛召军;张帆;明东;万柏坤. 基于人体轮廓面积特征的步态识别 [J]. 计算机工程, 2008, 34(1): 195-197.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于视角转换的多视角步态识别方法

Multi-view Gait Recognition Method Based on View Transformation

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献

相关文章 12

编辑推荐

Metrics

本文评价