2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

行人再识别技术综述

李幼蛟 卓力 张菁 李嘉锋 张辉

李幼蛟, 卓力, 张菁, 李嘉锋, 张辉. 行人再识别技术综述. 自动化学报, 2018, 44(9): 1554-1568. doi: 10.16383/j.aas.2018.c170505
引用本文: 李幼蛟, 卓力, 张菁, 李嘉锋, 张辉. 行人再识别技术综述. 自动化学报, 2018, 44(9): 1554-1568. doi: 10.16383/j.aas.2018.c170505
LI You-Jiao, ZHUO Li, ZHANG Jing, LI Jia-Feng, ZHANG Hui. A Survey of Person Re-identification. ACTA AUTOMATICA SINICA, 2018, 44(9): 1554-1568. doi: 10.16383/j.aas.2018.c170505
Citation: LI You-Jiao, ZHUO Li, ZHANG Jing, LI Jia-Feng, ZHANG Hui. A Survey of Person Re-identification. ACTA AUTOMATICA SINICA, 2018, 44(9): 1554-1568. doi: 10.16383/j.aas.2018.c170505

行人再识别技术综述

doi: 10.16383/j.aas.2018.c170505
基金项目: 

北京市教育委员会科技发展计划项目 KM201510005004

国家自然科学基金 61531006

北京市自然科学基金 4142009

北京市属高等学校高层次人才引进与培养计划项目 CIT & TCD20150311

北京市自然科学基金 4163071

北京市教育委员会科技发展计划项目 KM201410005002

北京市属高等学校人才强教计划资助项目PHR IHLB

国家自然科学基金 61471013

国家自然科学基金 61370189

北京市属高等学校高层次人才引进与培养计划项目 CIT & TCD201404043

国家自然科学基金 61372149

详细信息
    作者简介:

    李幼蛟 北京工业大学信息学部博士研究生.山东理工大学讲师.主要研究方向为计算机视觉, 深度学习.E-mail:liyoujiao@emails.bjut.edu.cn

    张菁 北京工业大学教授.2008年获得北京工业大学博士学位.美国德州大学圣安东尼奥分校计算机科学系访问学者.主要研究方向为图像处理, 图像识别, 图像检索.E-mail:zhj@bjut.edu.cn

    李嘉锋 北京工业大学信号与信息处理实验室讲师.2009年获得中国农业大学信息与电气工程学院学士学位, 2012年和2016年获得北京航空航天大学模式识别与智能系统专业硕士学位与博士学位.2014~2015年美国匹兹堡大学访问学者.主要研究方向为计算机视觉/图像增强, 图像复原.E-mail:lijiafeng@bjut.edu.cn

    张辉 北京工业大学信息学部讲师.2010年获得北京理工大学信号与信息处理专业博士学位.主要研究方向为计算机视觉, 机器学习在多媒体内容分析, 视觉追踪, 目标检测中的应用.E-mail:huizhang@bjut.edu.cn

    通讯作者:

    卓力 北京工业大学教授.1992年获得电子科技大学无线电技术系工学学士学位, 1998年和2004年分别获得东南大学信号与信息处理专业硕士学位和北京工业大学模式识别与智能系统专业博士学位.主要研究方向为图像/视频编码和传输, 多媒体内容分析, 多媒体信息安全.本文通信作者.E-mail:zhuoli@bjut.edu.cn

A Survey of Person Re-identification

Funds: 

Science and Technology Development Program of Beijing Education Committee KM201510005004

National Natural Science Foundation of China 61531006

Beijing Natural Science Foundation 4142009

the Importation Development of High-Caliber Talents Project of Beijing Municipal Institutions CIT & TCD20150311

Beijing Natural Science Foundation 4163071

Science and Technology Development Program of Beijing Education Committee KM201410005002

Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality IHLB

National Natural Science Foundation of China 61471013

National Natural Science Foundation of China 61370189

the Importation Development of High-Caliber Talents Project of Beijing Municipal Institutions CIT & TCD201404043

National Natural Science Foundation of China 61372149

More Information
    Author Bio:

    Ph.D. candidate at the Faculty of Information Technology, Beijing University of Technology and lecturer at Shandong University of Technology.His research interest covers computer vision and deep learning

    Professor at Beijing University of Technology, visiting scholar in the Department of Computer Science, University of Texas at San Antonio, USA.She received her Ph.D. degree from Beijing University of Technology in 2008.Her research interest covers image processing, image recognition, and image retrieval

    Lecturer at Signal and Information Processing Laboratory, Beijing University of Technology.He received his bachelor degree from the College of Information and Electrical Engineering, China Agriculture University in 2009, master degree and Ph.D.degree in pattern recognition and intelligence system from Beihang University in 2012 and 2016.He is a visiting scholar in the Department of Neurosurgery, University of Pittsburgh, USA from 2014 to 2015.His research interest covers computer vision, image enhancement, and image restoration

    Lecturer at the Faculty of Information, Beijing University of Technology. He received his Ph.D.degree in signal and information processing from Beijing Institute of Technology in 2010.His research interest covers computer vision and machine learning techniques applied to multimedia content analysis, visual tracking and object detection

    Corresponding author: ZHUO Li Professor at Beijing University of Technology.She received her bachelor degree in radio technology from University of Electronic Science and Technology in 1992, master degree in signal and information processing from Southeast University in 1998, and Ph.D.degree in pattern recognition and intellectual system from Beijing University of Technology in 2004. Her research interest covers image/video coding and transmission, multimedia content analysis, and multimedia information security. Corresponding author of this paper
  • 摘要: 行人再识别指的是判断不同摄像头下出现的行人是否属于同一行人, 可以看作是图像检索的子问题, 可以广泛应用于智能视频监控、安保、刑侦等领域.由于行人图像的分辨率变化大、拍摄角度不统一、光照条件差、环境变化大、行人姿态不断变化等原因, 使得行人再识别成为目前计算机视觉领域一个既具有研究价值又极具挑战性的研究热点和难点问题.早期的行人再识别方法大多基于人工设计特征, 在小规模数据集上开展研究.近年来, 大规模行人再识别数据集不断推出, 以及深度学习技术的迅猛发展, 为行人再识别技术的发展带来了新的契机.本文对行人再识别的发展历史、研究现状以及典型方法进行梳理和总结.首先阐述了行人再识别的基本研究框架, 然后分别针对行人再识别的两个关键技术(特征表达和相似性度量), 进行了归纳总结, 重点介绍了目前发展迅猛的深度学习技术在行人再识别中的应用.另外, 本文对行人再识别中代表性的数据集以及在各个数据集上可以取得优异性能的方法进行了分析和比较.最后对行人再识别技术的未来发展趋势进行了展望.
    1)  本文责任编委 黄庆明
  • 图  1  行人再识别典型流程图

    Fig.  1  Typical flowchart of person Re-ID

    图  2  行人图像块分割方法

    Fig.  2  Patch segmentation methods of pedestrian image

    图  3  行人显著区域示意图

    Fig.  3  The illustration of salient region

    图  4  深度学习模型各网络层示意图

    Fig.  4  Illustration of the network layers in deep learning model

    图  5  基于深度学习的行人再识别方法的三种方式

    Fig.  5  Three ways of deep learning-based person re-identification

    图  6  CMC曲线示意图

    Fig.  6  The illustration of CMC curve

    表  1  典型行人图像分割方法

    Table  1  Typical segmentation methods of pedestrian image

    分割方式 对应文献 主要思想
    上下半身分割 [3, 5] 提取行人的前景图像, 分成头部、躯干和腿部三部分.对后两部分计算垂直对称轴.对提取的特征根据与垂直对称轴的距离进行加权, 从而减少行人姿态变化的影响.缺点是分割过程过于复杂.
    条纹分割 [6-7] 分成六个水平条, 分别对应于行人头部、水平躯干的上下部、腿部的上下部分.然后提取水平条内的ELF特征, 减少了视角变化对识别的影响.缺点是会造成水平条内空间细节信息的损失.
    滑动窗分割 [8] 利用滑动窗来描述行人图像的局部细节信息, 在每个滑动窗内提取颜色和纹理特征.缺点是特征维数过大.
    三角形分割 [2] 利用局部运动特征对行人图像进行三角形时空分割.缺点是分割结果不够准确.
    下载: 导出CSV

    表  2  Market-1501数据集上不同深度模型对首轮识别率的影响

    Table  2  Rank-1 matching rates of different deep models in Market-1501

    模型名称 提出时间 首轮识别率(%)
    AlexNet[38] 2012年 56.03
    VGG-16[56] 2014年 64.34
    Residual-50[53] 2016年 72.54
    下载: 导出CSV

    表  3  基于深度学习的方法目前所取得的最好效果

    Table  3  The best results of deep learning-based methods

    整合方式 方法 取得最好效果的数据集 提出时间 首轮识别率(%)
    端到端式 TriNet[57] Market-1501, MARS 2017年 84.9, 79.8
    混合式 HIPHOP[58] VIPeR, CUHK01[59] 2017年 54.2, 78.8
    独立式 LCAR[60] iLIDS-VID[25] 2017年 60.02
    下载: 导出CSV

    表  4  常用行人再识别数据集及其参数

    Table  4  Popular person re-identification datasets and their parameters

    数据库名称 发布时间 图像/视频 人数 图像/视频片段数量 摄像头数量
    VIPeR 2007年 图像 632 1264 2
    CUHK01 2012年 图像 971 3884 2
    Market-1501 2015年 图像 1501 32668 6
    PRID-2011[65] 2011年 视频 200 400 2
    iLIDS-VID 2014年 视频 300 600 2
    MARS[4] 2016年 视频 1261 20715 6
    下载: 导出CSV

    表  5  行人再识别图像数据集上取得优异性能的方法对比

    Table  5  Comparison of state-of-the-art methods on image-based person re-identification datasets

    数据集 算法 人工设计/深度学习 rank-1(%) rank-5(%) rank-10(%) rank-20(%) 年份
    SCSP[66] 人工 53.5 82.6 91.5 96.6 2016年
    VIPeR FFN[50] 深度 51.1 81 91.4 96.9 2016年
    HIPHOP[58] 深度 54.2 82.4 91.5 96.9 2017年
    Zhang等[63] 人工 65 85 89.9 94.4 2016年
    CUHK01 FFN 深度 55.5 78.4 83.7 92.6 2016年
    HIPHOP 深度 78.8 92.6 95.3 97.8 2017年
    Zheng等[64] 深度 85.8 94.4 96.4 97.5 2016年
    Market-1501 SOMAnet[67] 深度 81.3 92.6 95.3 97.1 2017年
    WARCA[68] 人工 45.1 68.1 76 84 2016年
    下载: 导出CSV

    表  6  行人再识别视频数据集上取得优异性能的方法对比

    Table  6  Comparison of state-of-the-art methods on video-based person re-identification datasets

    数据集 算法 人工设计/深度学习 rank-1 (%) rank-5 (%) rank-10 (%) rank-20 (%) 年份
    zhang等[60] 深度 83.3 93.3 - 96.7 2017年
    PRID-2011 McLaughlin等[45] 深度 70 90 95 97 2016年
    TAPR[24] 人工 68.6 94.6 97.4 98.9 2016年
    Zhang等[60] 深度 60.2 85.1 - 94.2 2017年
    iLIDS-VID McLaughlin等[45] 深度 58 84 91 96 2016年
    TAPR 人工 55 87.5 93.8 97.2 2016年
    Zhang等[60] 深度 55.5 70.2 - 80.2 2017年
    MARS CNN+XQDA[4] 深度 65.3 80.2 - 89 2016年
    LOMO+XQDA[4] 人工 30.7 46.6 - 60.9 2016年
    下载: 导出CSV
  • [1] Porikli F.Inter-camera color calibration by correlation model function.In: Proceedings of the 2003 International Conference on Image Processing.Barcelona, Spain: IEEE, 2003.Ⅱ-133-6
    [2] Gheissari N, Sebastian T B, Hartley R.Person reidentification using spatiotemporal appearance.In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.New York, USA: IEEE, 2006.1528-1535
    [3] Farenzena M, Bazzani L, Perina A, Murino V, Cristani M.Person re-identification by symmetry-driven accumulation of local features.In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition.San Francisco, CA, USA: IEEE, 2010.2360-2367
    [4] Zheng L, Bie Z, Sun Y F, Wang J D, Su C, Wang S J, et al.MARS: a video benchmark for large-scale person re-identification.In: Proceedings of the 14th European Conference on Computer Vision.Amsterdam, Netherlands: Springer, 2016.868-884
    [5] Bazzani L, Cristani M, Murino V.Symmetry-driven accumulation of local features for human characterization and re-identification.Computer Vision and Image Understanding, 2013, 117 (2):130-144 doi: 10.1016/j.cviu.2012.10.008
    [6] Gray D, Tao H.Viewpoint invariant pedestrian recognition with an ensemble of localized features.In: Proceedings of the 10th European Conference on Computer Vision.Marseille, France: Springer, 2008.262-275
    [7] Zheng W S, Gong S G, Xiang T.Reidentification by relative distance comparison.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35 (3):653-668 doi: 10.1109/TPAMI.2012.138
    [8] Liao S C, Hu Y, Zhu X Y, Li S Z.Person re-identification by local maximal occurrence representation and metric learning.In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston, MA, USA: IEEE, 2015.2197-2206
    [9] Zeng M Y, Wu Z M, Tian C, Zhang L, Hu L.Efficient person re-identification by hybrid spatiogram and covariance descriptor.In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops.Boston, MA, USA: IEEE, 2015.48-56
    [10] Ma B P, Su Y, Jurie F.Covariance descriptor based on bio-inspired features for person re-identification and face verification.Image and Vision Computing, 2014, 32(6-7):379-390 doi: 10.1016/j.imavis.2014.04.002
    [11] Matsukawa T, Okabe T, Suzuki E, Sato Y.Hierarchical Gaussian descriptor for person re-identification.In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas, NV, USA: IEEE, 2016.1363-1372
    [12] Zhao R, Ouyang W L, Wang X G.Person re-identification by saliency learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39 (2):356-370 doi: 10.1109/TPAMI.2016.2544310
    [13] Kviatkovsky I, Adam A, Rivlin E.Color invariants for person reidentification.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35 (7):1622-1634 doi: 10.1109/TPAMI.2012.246
    [14] 齐美彬, 檀胜顺, 王运侠, 刘皓, 蒋建国.基于多特征子空间与核学习的行人再识别.自动化学报, 2016, 42 (2):229-308 http://www.aas.net.cn/CN/abstract/abstract18819.shtml

    Qi Mei-Bin, Tan Sheng-Shun, Wang Yun-Xia, Liu Hao, Jiang Jian-Guo.Multi-feature subspace and kernel learning for person re-identification.Acta Automatica Sinica, 2016, 42 (2):229-308 http://www.aas.net.cn/CN/abstract/abstract18819.shtml
    [15] Zhao R, Ouyang W L, Wang X G.Unsupervised salience learning for person re-identification.In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition.Portland, OR, USA: IEEE, 2013.3586-3593
    [16] Zhao R, Ouyang W L, Wang X G.Learning mid-level filters for person re-identification.In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition.Columbus, OH, USA: IEEE, 2014.144-151
    [17] Gong S G, Cristani M, Yan S C, Loy C C.Person Re-Identification.London:Springer, 2014.139-160
    [18] Layne R, Hospedales T M, Gong S G.Person re-identification by attributes.In: Proceedings of the 2012 British Machine Vision Conference.Surrey, UK: BMVA Press, 2012.
    [19] Shi Z Y, Hospedales T M, Xiang T.Transferring a semantic representation for person re-identification and search.In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston, MA, USA: IEEE, 2015.4184-4193
    [20] Su C, Yang F, Zhang S L, Tian Q, Davis L S, Gao W.Multi-task learning with low rank attribute embedding for person re-identification.In: Proceedings of the 2015 IEEE International Conference on Computer Vision.Santiago, Chile: IEEE, 2015.3739-3747
    [21] Caruana R A.Multitask learning: a knowledge-based source of inductive bias.In: Proceedings of the 10th International Conference on Machine Learning.Amherst, USA: Elsevier, 1993.41-48
    [22] Gray D, Brennan S, Tao H.Evaluating appearance models for recognition, reacquisition, and tracking.In: Proceedings of the 10th International Workshop on Performance Evaluation for Tracking and Surveillance.Rio de Janeiro, Brazil: IEEE, 2007.1-7
    [23] You J J, Wu A C, Li X, Zheng W S.Top-push video-based person re-identification.In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas, NV, USA: IEEE, 2016.1345-1353
    [24] Gao C X, Wang J, Liu L Y, Yu J G, Sang N.Temporally aligned pooling representation for video-based person re-identification.In: Proceedings of the 2016 International Conference on Image Processing.Phoenix, AZ, USA: IEEE, 2016.4284-4288
    [25] Wang T Q, Gong S G, Zhu X T, Wang S J.Person re-identification by video ranking.In: Proceedings of the 13th European Conference on Computer Vision.Zurich, Switzerland: Springer, 2014.688-703
    [26] Man J, Bhanu B.Individual recognition using gait energy image.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28 (2):316-322 doi: 10.1109/TPAMI.2006.38
    [27] Klaser A, Marszalek M, Schmid C.A spatio-temporal descriptor based on 3D-gradients.In: Proceedings of the 19th British Machine Vision Conference.Leeds, UK: British Machine Vision Association, 2008, 275: 1-10
    [28] Bhattachayya A.On a measure of divergence between two statistical populations defined by their probability distributions.Bulletin Calcutta Mathematical Society, 1943, 35:99-109 https://www.sciencedirect.com/science/article/pii/0022247X89903351
    [29] De Maesschalck R, Jouan-Rimbaud D, Massart D L.The mahalanobis distance.Chemometrics and Intelligent Laboratory Systems, 2000, 50 (1):1-18 doi: 10.1016/S0169-7439(99)00047-7
    [30] Xing E P, Ng A Y, Jordan M I, Russell S J.Distance metric learning, with application to clustering with side-information.In: Proceedings of the 15th International Conference on Neural Information Processing Systems.Cambridge, MA, USA: MIT Press, 2002.521-528
    [31] Zheng W S, Gong S G, Xiang T.Person re-identification by probabilistic relative distance comparison.In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition.Colorado Springs, CO, USA: IEEE, 2011.649-656
    [32] Weinberger K Q, Saul L K.Fast solvers and efficient implementations for distance metric learning.In: Proceedings of the 25th International Conference on Machine Learning.Helsinki, Finland: ACM, 2008.1160-1167
    [33] Davis J V, Kulis B, Jain P, Sra S, Dhillon I S.Information-theoretic metric learning.In: Proceedings of the 24th International Conference on Machine Learning.Corvalis, Oregon, USA: ACM, 2007.209-216
    [34] Guillaumin M, Verbeek J, Schmid C.Is that you? Metric learning approaches for face identification.In: Proceedings of the 12th International Conference on Computer Vision.Kyoto, Japan: IEEE, 2009.498-505
    [35] Köestinger M, Hirzer M, Wohlhart P, Roth P M, Bischof H.Large scale metric learning from equivalence constraints.In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition.Providence, RI, USA: IEEE, 2012.2288-2295
    [36] Karanam S, Li Y, Radke R J.Sparse re-id: block sparsity for person re-identification.In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops.Boston, MA, USA: IEEE, 2015.33-40
    [37] Karanam S, Li Y, Radke R J.Person re-identification with discriminatively trained viewpoint invariant dictionaries.In: Proceedings of the 2015 IEEE International Conference on Computer Vision.Santiago, Chile: IEEE, 2015.4516-4524
    [38] Krizhevsky A, Sutskever I, Hinton G E.Imagenet classification with deep convolutional neural networks.In: Proceedings of the 25th International Conference on Neural Information Processing Systems.Lake Tahoe, Nevada, USA: Curran Associates Inc., 2012.1097-1105
    [39] 管皓, 薛向阳, 安志勇.深度学习在视频目标跟踪中的应用进展与展望.自动化学报, 2016, 42 (6):834-847 http://www.aas.net.cn/CN/abstract/abstract18874.shtml

    Guan Hao, Xue Xiang-Yang, An Zhi-Yong.Advances on application of deep learning for video object tracking.Acta Automatica Sinica, 2016, 42 (6):834-847 http://www.aas.net.cn/CN/abstract/abstract18874.shtml
    [40] 常亮, 邓小明, 周明全, 武仲科, 袁野, 杨硕, 等.图像理解中的卷积神经网络.自动化学报, 2016, 42 (9):1300-1312 http://www.aas.net.cn/CN/abstract/abstract18919.shtml

    Chang Liang, Deng Xiao-Ming, Zhou Ming-Quan, Wu Zhong-Ke, Yuan Ye, Yang Shuo, et al.Convolutional neural networks in image understanding.Acta Automatica Sinica, 2016, 42 (9):1300-1312 http://www.aas.net.cn/CN/abstract/abstract18919.shtml
    [41] 段艳杰, 吕宜生, 张杰, 赵学亮, 王飞跃.深度学习在控制领域的研究现状与展望.自动化学报, 2016, 42 (5):643-654 http://www.aas.net.cn/CN/abstract/abstract18852.shtml

    Duan Yan-Jie, Lv Yi-Sheng, Zhang Jie, Zhao Xue-Liang, Wang Fei-Yue.Deep learning for control:the state of the art and prospects.Acta Automatica Sinica, 2016, 42 (5):643-654 http://www.aas.net.cn/CN/abstract/abstract18852.shtml
    [42] 金连文, 钟卓耀, 杨钊, 杨维信, 谢泽澄, 孙俊.深度学习在手写汉字识别中的应用综述.自动化学报, 2016, 42 (8):1125-1141 http://www.aas.net.cn/CN/abstract/abstract18903.shtml

    Jin Lian-Wen, Zhong Zhuo-Yao, Yang Zhao, Yang Wei-Xin, Xie Ze-Cheng, Sun Jun.Applications of deep learning for handwritten Chinese character recognition:a review.Acta Automatica Sinica, 2016, 42 (8):1125-1141 http://www.aas.net.cn/CN/abstract/abstract18903.shtml
    [43] Li W, Zhao R, Xiao T, Wang X G.DeepReID: deep filter pairing neural network for person re-identification.In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition.Columbus, OH, USA: IEEE, 2014.152-159
    [44] Ahmed E, Jones M, Marks T K.An improved deep learning architecture for person re-identification.In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston, MA, USA: IEEE, 2015.3908-3916
    [45] McLaughlin N, Martinez Del Rincon J, Miller P.Recurrent convolutional network for video-based person re-identification.In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas, NV, USA: IEEE, 2016.1325-1334
    [46] Yan Y C, Ni B B, Song Z C, Ma C, Yan Y, Yang X K.Person re-identification via recurrent feature aggregation.In: Proceedings of the 14th European Conference on Computer Vision.Amsterdam, Netherlands: Springer, 2016.701-716
    [47] Cheng D, Gong Y H, Zhou S P, Wang J J, Zheng N N.Person re-identification by multi-channel parts-based CNN with improved triplet loss function.In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas, NV, USA: IEEE, 2016.1335-1344
    [48] Wu S X, Chen Y C, Li X, Wu A C, You J J, Zheng W S.An enhanced deep feature representation for person re-identification.In: Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision.Lake Placid, NY, USA: IEEE, 2016.1-8
    [49] Li Y J, Zhuo L, Hu X C, Zhang J.A combined feature representation of deep feature and hand-crafted features for person re-identification.In: Proceedings of the 2016 International Conference on Progress in Informatics and Computing.Shanghai, China: IEEE, 2016.224-227
    [50] Chan T H, Jia K, Gao S H, Lu J W, Zeng Z N, Ma Y.PCANet:a simple deep learning baseline for image classification? IEEE Transactions on Image Processing, 2015, 24 (12):5017-5032 http://d.old.wanfangdata.com.cn/Periodical/dianzixb201608028
    [51] Zheng L, Wang S J, Tian L, He F, Liu Z Q, Tian Q.Query-adaptive late fusion for image search and person re-identification.In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston, MA, USA: IEEE, 2015.1741-1750
    [52] Zheng L, Huang Y J, Lu H C, Yang Y.Pose invariant embedding for deep person re-identification.arXiv preprint, arXiv: 1701.07732, 2017.
    [53] He K M, Zhang X Y, Ren S Q, Sun J.Deep residual learning for image recognition.In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas, NV, USA: IEEE, 2016.770-778
    [54] Zheng L, Zhang H H, Sun S Y, Chandraker M, Yang Y, Tian Q.Person re-identification in the wild.arXiv preprint, arXiv: 1604.02531, 2016.
    [55] Zheng L, Shen L Y, Tian L, Wang S L, Wang J D, Tian Q.Scalable person re-identification: a benchmark.In: Proceedings of the 2015 IEEE International Conference on Computer Vision.Santiago, Chile: IEEE, 2015.1116-1124
    [56] Simonyan K, Zisserma A.Very deep convolutional networks for large-scale image recognition.arXiv preprint, arXiv: 1409.1556, 2014.
    [57] Hermans A, Beyer L, Leibe B.In defense of the triplet loss for person re-identification.arXiv preprint, arXiv: 1703.07737, 2017.
    [58] Chen Y C, Zhu X T, Zheng W S, Lai J H.Person re-identification by camera correlation aware feature augmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40 (2):392-408 doi: 10.1109/TPAMI.2017.2666805
    [59] Li W, Zhao R, Wang X G.Human reidentification with transferred metric learning.In: Proceedings of the 11th Asian Conference on Computer Vision.Daejeon, Korea: Springer, 2012.31-44
    [60] Zhang W, Hu S N, Liu K.Learning compact appearance representation for video-based person re-identification.arXiv preprint, arXiv: 1702.06294, 2017.
    [61] Su C, Zhang S L, Xing J L, Gao W, Tian Q.Deep attributes driven multi-camera person re-identification.In: Proceedings of the 14th European Conference on Computer Vision.Amsterdam, Netherlands: Springer, 2016.475-491
    [62] Zhu J Q, Liao S C, Yi D, Lei Z, Li S Z.Multi-label CNN based pedestrian attribute learning for soft biometrics.In: Proceedings of the 2015 International Conference on Biometrics.Phuket, Thailand: IEEE, 2015.535-540
    [63] Zhang L, Xiang T, Gong S G.Learning a discriminative null space for person re-identification.In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas, NV, USA: IEEE, 2016.1239-1248
    [64] Zheng Z D, Zheng L, Yang Y.A discriminatively learned CNN embedding for person re-identification.arXiv preprint, arXiv: 1611.05666, 2016.
    [65] Hirzer M, Beleznai C, Roth P M, Bischof H.Person re-identification by descriptive and discriminative classification.In: Proceedings of the 17th Scandinavian Conference on Image Analysis.Ystad, Sweden: Springer, 2011.91-102
    [66] Chen D P, Yuan Z J, Chen B D, Zheng N N.Similarity learning with spatial constraints for person re-identification.In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas, NV, USA: IEEE, 2016.1268-1277
    [67] Barbosa I B, Cristani M, Caputo B, Rognhaugen A, Theoharis T.Looking beyond appearances: synthetic training data for deep CNNs in re-identification.arXiv preprint, arXiv: 1701.03153, 2017.
    [68] Jose C, Fleuret F.Scalable metric learning via weighted approximate rank component analysis.In: Proceedings of the 14th European Conference on Computer Vision.Amsterdam, Netherlands: Springer, 2016.875-890
    [69] Yu D, Li J.Recent progresses in deep learning based acoustic models.IEEE/CAA Journal of Automatica Sinica, 2017, 4(3), 396-409 doi: 10.1109/JAS.2017.7510508
  • 加载中
图(6) / 表(6)
计量
  • 文章访问数:  3741
  • HTML全文浏览量:  2378
  • PDF下载量:  2232
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-09-05
  • 录用日期:  2018-01-19
  • 刊出日期:  2018-09-20

目录

    /

    返回文章
    返回