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基于深度学习的行人重识别研究进展

罗浩 姜伟 范星 张思朋

罗浩, 姜伟, 范星, 张思朋. 基于深度学习的行人重识别研究进展. 自动化学报, 2019, 45(11): 2032-2049. doi: 10.16383/j.aas.c180154
引用本文: 罗浩, 姜伟, 范星, 张思朋. 基于深度学习的行人重识别研究进展. 自动化学报, 2019, 45(11): 2032-2049. doi: 10.16383/j.aas.c180154
LUO Hao, JIANG Wei, FAN Xing, ZHANG Si-Peng. A Survey on Deep Learning Based Person Re-identification. ACTA AUTOMATICA SINICA, 2019, 45(11): 2032-2049. doi: 10.16383/j.aas.c180154
Citation: LUO Hao, JIANG Wei, FAN Xing, ZHANG Si-Peng. A Survey on Deep Learning Based Person Re-identification. ACTA AUTOMATICA SINICA, 2019, 45(11): 2032-2049. doi: 10.16383/j.aas.c180154

基于深度学习的行人重识别研究进展

doi: 10.16383/j.aas.c180154
基金项目: 

浙江省基础公益研究计划项目 LGF18F030002

国家自然科学基金 61633019

国家自然科学基金 61375049

详细信息
    作者简介:

    罗浩  浙江大学控制科学与工程学院智能系统与控制研究所博士研究生.2015年获得浙江大学控制科学与工程学士学位.主要研究方向为行人重识别, 多目标跟踪, 深度学习, 计算机视觉方向.E-mail:haoluocsc@zju.edu.cn

    范星  浙江大学控制科学与工程学院博士研究生.2015年获得浙江大学控制科学与工程学士学位.主要研究方向为行人重识别.E-mail:xfanplus@zju.edu.cn

    张思朋  2016年获得浙江大学控制科学与工程硕士学位.主要研究方向为计算机视觉, 行人重识别.E-mail:zhangsipeng@zju.edu.cn

    通讯作者:

    姜伟  浙江大学控制科学与工程学院智能系统与控制研究所副教授.2005年获得日本东京工业大学博士学位.主要研究方向为机器视觉, 计算机图形学, 机器学习.本文通信作者.E-mail:jiangwei_zju@zju.edu.cn

  • 本文责任编委 赖剑煌

A Survey on Deep Learning Based Person Re-identification

Funds: 

Zhejiang Basic Public Welfare Research Project LGF18F030002

National Natural Science Foundation of China 61633019

National Natural Science Foundation of China 61375049

More Information
    Author Bio:

    Ph. D. candidate at the Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University. He received his bachelor degree from the College of Control Science and Engineering, Zhejiang University in 2015. His research interest covers person re-identiflcation, multi-target multi-camera tracking, deep learning and computer vision

    Ph. D. candidate at the College of Control Science and Engineering, Zhejiang University. He received his bachelor degree from the College of Control Science and Engineering, Zhejiang University in 2015. His main research interest is person re-identiflcation

    She received her master degree from the College of Control Science and Engineering, Zhejiang University in 2016. Her research interest covers computer vision and person re-identiflcation

    Corresponding author: JIANG Wei Associate professor at the Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University. He received his Ph. D. degree from Tokyo Institute of Technology, Japan in 2005. His research interest covers machine vision, computer graphics and machine learning. Corresponding author of this paper
  • Recommended by Associate Editor LAI Jian-Huang
  • 摘要: 行人重识别是计算机视觉领域近年来非常热的一个研究课题,可以被视为图像检索的一个子问题,其目标是给定一个监控行人图像检索跨设备下的该行人图像.传统的方法依赖手工特征,不能适应数据量很大的复杂环境.近年来随着深度学习的发展,大量基于深度学习的行人重识别方法被提出.本文先简单介绍了该问题的定义及传统方法的局限,并列举了一些适用于深度学习方法的行人重识别数据集.此外我们详细地总结了一些比较典型的基于深度学习的行人重识别方法,并比较了部分算法在Market1501数据集上的性能表现.最后我们对该问题未来的研究方向做了一个展望.
    Recommended by Associate Editor LAI Jian-Huang
    1)  本文责任编委 赖剑煌
  • 图  1  行人重识别系统

    Fig.  1  Person ReID system

    图  2  行人重识别数据集图片及难点示例

    Fig.  2  The examples of images and challenge of person ReID datasets

    图  3  结合ID损失和属性损失网络示例[26]

    Fig.  3  The example network with identification loss and attribute loss[26]

    图  4  结合验证损失和ID损失网络示例[25]

    Fig.  4  The example network with verification loss and identification loss[25]

    图  5  度量学习方法样本符号示例图

    Fig.  5  The sample$'$s label of metric learning

    图  6  利用图片切块提取局部特征示例[34]

    Fig.  6  The example of extracting local features with image blocks[34]

    图  7  利用姿态点提取局部特征示例[45]

    Fig.  7  The example of extracting local features with pose points[45]

    图  8  融合内容信息和运动信息的AMOC网络[49]

    Fig.  8  The AMOC network which fusions context information and motion information[49]

    图  9  RQEN与平均池化注意力图对比[18]

    Fig.  9  The attention maps of RQEN and average pooling[18]

    图  10  CycleGAN进行图片风格转换流程图$(A\rightarrow B) $

    Fig.  10  The pipeline of image style transfer using CycleGAN $(A\rightarrow B)$

    图  11  GAN网络生成行人图片示例

    Fig.  11  The examples of pedestrian images by GAN generated

    图  12  PSE网络流程图[71]

    Fig.  12  The pipeline of PSE network[71]

    图  13  图像超分辨和行人身份识别联合学习模型示意图[72]

    Fig.  13  Illustration of model structure of image super resolution and person identity joint learning[72]

    图  14  深度空间特征重建方法示意图[73]

    Fig.  14  Illustration of deep spatial feature reconstruction method[73]

    图  15  深度零填充模型详解[17]

    Fig.  15  Explanation of deep zero padding model[17]

    表  1  典型行人重识别数据集

    Table  1  Typical ReID datasets

    数据集 发布时间 ID数 图片数 序列数 室内相机 室外相机 检测器 评估
    ViPeR 2007 632 1 264 × 0 2 手动 CMC
    PRID2011 2011 934 24 541 400 0 2 手动 CMC
    CUHK03 2014 1 467 13 164 × 10 0 手动+ DPM CMC + mAP
    Market1501 2015 1 501 32 217 × 0 6 手动+ DPM CMC + mAP
    CUHK-SYSU 2016 8 432 99 809 × 0 0 DPM CMC + mAP
    MARS 2016 1 261 1 119 003 20 715 0 6 DPM + GMMCP CMC + mAP
    DukeMTMC-reID 2017 1 812 36 441 × 0 8 手动 CMC + mAP
    SYSU-MM01 2017 491 287 628 × 3 3 未知 CMC + mAP
    LPW 2018 2 731 590 000+ 7 694 0 11 手动+ DPM CMC + mAP
    MSMT17 2018 4 101 126 441 × 3 12 Faster RCNN CMC + mAP
    LVreID 即将发布 3 772 2 989 436 14 943 3 12 Faster RCNN CMC + mAP
    下载: 导出CSV

    表  2  基于GAN网络的方法比较

    Table  2  The comparison of GAN based methods

    算法 GAN CycleGAN PTGAN SPGAN PNGAN
    基础 GAN CycleGAN CycleGAN CycleGAN InfoGAN
    额外 标签平滑 标签平滑 前景分割 孪生网络 姿态估计
    目标 数据增广 相机偏差 数据域偏差 数据域偏差 姿态偏差
    下载: 导出CSV

    表  3  基于深度学习的行人重识别方法总结比较

    Table  3  Comparison of deep learning based ReID methods

    方法 表征学习 度量学习 全局特征 局部特征 单帧图像 视频序列
    IDE Net[29]
    TriHard[37]
    QuadLoss[40]
    LSTM Siamese[34]
    Gate Reid[33]
    Spindle Net[45]
    GLAD [44]
    AlignedReID[43]
    AMOC[49]
    RQEN[43]
    下载: 导出CSV

    表  4  典型行人重识别方法在Market1501上性能比较

    Table  4  Comparison of the performance of typical ReID methods on Market1501

    方法 rank-1 mAP 损失函数 基础网络 简单描述 发表
    LOMO + XQDA[7] 43.8 22.2 传统方法基准 CVPR2015
    LSTM Siamese[34] 61.6 35.3 对比损失 LSTM 图像分块+孪生网络 ECCV2016
    Gate Reid[33] 65.9 39.6 对比损失 CNN 孪生网络+多尺度全局特征个 ECCV2016
    Spindle Net[45] 76.9 - 分类损失 CNN 姿态对齐+ IDE CVPR2017
    GAN[39] 78.1 56.2 分类损失 Resnet50 GAN + IDE +数据增广 ICCV2017
    Part-Aligned[67] 81.0 63.4 三元组损失 GoogleNet 姿态对齐+度量学习 ICCV2017
    Deep Transfer[25] 83.7 65.5 分类损失 GoogleNet ID损失+验证损失+迁移学习 Arxiv2016
    TriHard[37] 84.9 69.1 三元组损失 Resnet50 难样本挖掘+三元组损失 Arxiv2017
    DML[28] 87.7 68.8 分类损失 MobileNets[68] IDE +互学习 CVPR2018
    CamStyle[65] 88.1 68.7 分类损失 Resnet50 CycleGAN + IDE +相机偏差 CVPR2018
    GLAD [44] 89.9 73.9 分类损失 GoogleNet 姿态对齐+特征融合+重检索 ACMMM2017
    AlignedReID[43] 91.8 79.8 三元组损失 Resnet50 难样本挖掘+图片切块+自动对齐+互学习 Arxiv2017
    PNGAN[64] 95.5 89.9 分类损失 Resnet50 InfoGAN +姿态估计+ IDE +属性损失 Arxiv2017
    SPGAN[62] 51.5 22.8 分类损失 Resnet50 CycleGAN + IDE +数据域偏差+无监督 CVPR2018
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-03-19
  • 录用日期:  2018-09-14
  • 刊出日期:  2019-11-20

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