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

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

罗浩^1,,
姜伟^1, ,,
范星^1,,
张思朋^1,

1.
浙江大学智能系统与控制研究所杭州 310027

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

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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

A Survey on Deep Learning Based Person Re-identification

LUO Hao^1
,,
JIANG Wei^{1
, ,},
FAN Xing^1
,,
ZHANG Si-Peng^1
,

1.
Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027

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

摘要

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.
- 行人重识别 /
- 深度学习 /
- 计算机视觉 /
- 卷积神经网络
Abstract: Person re-identification (ReID) is a popular research topic in computer vision. It aims to retrieve the given pedestrian image across the device, which can be regarded as a sub-problem of image retrieval. The traditional methods rely on hand-crafted features and can not adapt to the complicated environment with a large number of data. In recent years, with the development of deep learning, a large number of ReID methods based on deep learning have been proposed. This paper briefly introduces the definition of the problem and the limitations of the traditional methods, and then lists some popular databases suitable for deep learning. Moreover, we summarize some typical deep learning based methods in detail, and compare the performance of some algorithms on Market1501. Finally, we make a prospect for the future research direction of person ReID.
- Person re-identification /
- deep learning /
- computer vision /
- convolutional neural networks
Recommended by Associate Editor LAI Jian-Huang
注释:

1) 本文责任编委赖剑煌

HTML全文

图 1 行人重识别系统

Fig. 1 Person ReID system

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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

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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]

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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]

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图 5 度量学习方法样本符号示例图

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

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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]

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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]

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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]

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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]

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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 $(A\rightarrow B)$

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图 11 GAN网络生成行人图片示例

Fig. 11 The examples of pedestrian images by GAN generated

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图 12 PSE网络流程图^[71]

Fig. 12 The pipeline of PSE network^[71]

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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]

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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]

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图 15 深度零填充模型详解^[17]

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

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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

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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

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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]	√	√	√	√		√

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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

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