Deep Learning Based Occluded Person Re-Identification: A Survey

doi:10.1145/3610534

	Deep Learning Based Occluded Person Re-Identification: A Survey
	Peng, Yunjie 1; Wu, Jinlin2 ; Xu, Boqiang2 ; Cao, Chunshui3 ; Liu, Xu3 ; Sun, Zhenan4 ; He, Zhiqiang 1
发表期刊	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
ISSN	1551-6857
	2024-03-01
卷号	20 期号:3 页码:27
通讯作者	Peng, Yunjie(yunjiepeng@buaa.edu.cn)
摘要	Occluded person re-identification (Re-ID) focuses on addressing the occlusion problem when retrieving the person of interest across non-overlapping cameras. With the increasing demand for intelligent video surveillance and the application of person Re-ID technology, the real-world occlusion problem draws considerable interest from researchers. Although a large number of occluded person Re-ID methods have been proposed, there are few surveys that focus on occlusion. To fill this gap and help boost future research, this article provides a systematic survey of occluded person Re-ID. In this work, we review recent deep learning based occluded person Re-ID research. First, we summarize the main issues caused by occlusion as four groups: position misalignment, scale misalignment, noisy information, and missing information. Second, we categorize existing methods into six solution groups: matching, image transformation, multi-scale features, attention mechanism, auxiliary information, and contextual recovery. We also discuss the characteristics of each approach, as well as the issues they address. Furthermore, we present the performance comparison of recent occluded person Re-ID methods on four public datasets: Partial-ReID, Partial-iLIDS, Occluded-ReID, and Occluded-DukeMTMC. We conclude the study with thoughts on promising future research directions.
关键词	Occluded person re-identification partial person re-identification literature survey deep learning
DOI	10.1145/3610534
关键词[WOS]	NETWORK
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:001153381000013
出版者	ASSOC COMPUTING MACHINERY
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55584
专题	多模态人工智能系统全国重点实验室模式识别实验室
通讯作者	Peng, Yunjie
作者单位	1.Beihang Univ, Sch Comp Sci & Technol, 37 XueYuan Rd, Beijing 100191, Peoples R China 2.Chinese Acad Sci, Inst Automat, 95 ZhongGuanCun East Rd, Beijing 100190, Peoples R China 3.Watrix Technol Ltd Co Ltd, 51 XueYuan Rd, Beijing 100191, Peoples R China 4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Ctr Res Intelligent Percept & Comp, 95 ZhongGuanCun East Rd, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Peng, Yunjie,Wu, Jinlin,Xu, Boqiang,et al. Deep Learning Based Occluded Person Re-Identification: A Survey[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2024,20(3):27.
APA	Peng, Yunjie.,Wu, Jinlin.,Xu, Boqiang.,Cao, Chunshui.,Liu, Xu.,...&He, Zhiqiang.(2024).Deep Learning Based Occluded Person Re-Identification: A Survey.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,20(3),27.
MLA	Peng, Yunjie,et al."Deep Learning Based Occluded Person Re-Identification: A Survey".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 20.3(2024):27.