行人再识别技术综述

李幼蛟; 卓力; 张菁; 李嘉锋; 张辉

doi:10.16383/j.aas.2018.c170505

行人再识别技术综述

doi: 10.16383/j.aas.2018.c170505

李幼蛟^1,2,3,,
卓力^1,2,4, ,,
张菁^1,2,,
李嘉锋^1,2,,
张辉^1,2,

1.
北京工业大学计算智能与智能系统北京市重点实验室北京 100124
2.
北京工业大学信息学部微电子学院北京 100124
3.
山东理工大学计算机科学与技术学院淄博 255000
4.
北京电动车辆协同创新中心北京 100081

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

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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

A Survey of Person Re-identification

LI You-Jiao^{1,2,3
,},
ZHUO Li^{1,2,4
, ,},
ZHANG Jing^{1,2
,},
LI Jia-Feng^{1,2
,},
ZHANG Hui^{1,2
,}

1.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124
2.
College of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing 100124
3.
College of Computer Science and Technology, Shandong University of Technology, Zibo 255000
4.
Beijing Collaborative Innovation Center of Electric Vehicles, Beijing 100081

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

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.
- 行人再识别 /
- 人工设计特征 /
- 深度学习 /
- 特征表达 /
- 相似性度量
Abstract: Person re-identification aims to associate the same person across different views and can be taken as a subproblem of image retrieval.It has extensive application prospects in many areas such as intelligent video surveillance, security, and criminal investigation.Due to poor illumination condition, image resolution, camera viewpoint, environment, and pedestrian pose, person re-identification has become one of the challenging problems in computer vision.Early person re-identification methods mostly rely on hand-crafted features and researches are conducted on small-scale datasets.In recent years, the emergence of large-scale datasets and rapid development of deep learning techniques provide person re-identification with new opportunities.This survey gives a detailed overview of the history, state of the art, and typical methods in this domain.Firstly, the general framework of person re-identification is presented.Then, feature representation, similarity measurement, and two key aspects of person re-identification, are further summarized, respectively.We also highlight the application of rapid developing deep learning techniques to person re-identification.Moreover, the representative datasets of person re-identification and methods of obtaining excellent performance on each dataset are analyzed and compared.Finally, the future trends of this field are discussed.
- Person re-identification /
- hand-crafted feature /
- deep learning /
- feature representation /
- similarity measurement
注释:

1) 本文责任编委黄庆明

HTML全文

图 1 行人再识别典型流程图

Fig. 1 Typical flowchart of person Re-ID

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图 2 行人图像块分割方法

Fig. 2 Patch segmentation methods of pedestrian image

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图 3 行人显著区域示意图

Fig. 3 The illustration of salient region

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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

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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

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图 6 CMC曲线示意图

Fig. 6 The illustration of CMC curve

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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.

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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

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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

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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

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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

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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

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