GLAD: Global-Local-Alignment Descriptor for Scalable Person Re-Identification

doi:10.1109/TMM.2018.2870522

	GLAD: Global-Local-Alignment Descriptor for Scalable Person Re-Identification
	Wei, Longhui 1; Zhang, Shiliang 1; Yao, Hantao2 ; Gao, Wen 1; Tian, Qi 3,4
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
ISSN	1520-9210
	2019-04-01
卷号	21 期号:4 页码:986-999
通讯作者	Zhang, Shiliang(slzhang.jdl@pku.edu.cn)
摘要	The huge variance of human pose and the misalignment of detected human images significantly increase the difficulty of pedestrian image matching in person Re-Identification (Re-ID). Moreover, the massive visual data being produced by surveillance video cameras requires highly efficient person Re-ID systems. Targeting to solve the first problem, thiswork proposes a robust and discriminative pedestrian image descriptor, namely, the Global-Local-Alignment Descriptor (GLAD). For the second problem, this work treats person Re-ID as image retrieval and proposes an efficient indexing and retrieval framework. GLAD explicitly leverages the local and global cues in the human body to generate a discriminative and robust representation. It consists of part extraction and descriptor learning modules, where several part regions are first detected and then deep neural networks are designed for representation learning on both the local and global regions. A hierarchical indexing and retrieval framework is designed to perform offline relevance mining to eliminate the huge person ID redundancy in the gallery set, and accelerate the online Re-ID procedure. Extensive experimental results on widely used public benchmark datasets show GLAD achieves competitive accuracy compared to the state-of-the-art methods. On a large-scale person, with the Re-ID dataset containing more than 520 K images, our retrieval framework significantly accelerates the online Re-ID procedure while also improving Re-ID accuracy. Therefore, this work has the potential to work better on person Re-ID tasks in real scenarios.
关键词	Person re-identification (Re-ID) global-local-alignment descriptor retrieval framework
DOI	10.1109/TMM.2018.2870522
关键词[WOS]	IDENTIFICATION ; PERFORMANCE
收录类别	SCI
语种	英语
资助项目	NVIDIA NVAIL program ; NVIDIA NVAIL program
项目资助者	NVIDIA NVAIL program
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000462413700014
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：114[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/173211/23499
专题	多模态人工智能系统全国重点实验室_多媒体计算
通讯作者	Zhang, Shiliang
作者单位	1.Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Huawei, Noahs Ark Lab, Shenzhen 518129, Peoples R China 4.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
推荐引用方式 GB/T 7714	Wei, Longhui,Zhang, Shiliang,Yao, Hantao,et al. GLAD: Global-Local-Alignment Descriptor for Scalable Person Re-Identification[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2019,21(4):986-999.
APA	Wei, Longhui,Zhang, Shiliang,Yao, Hantao,Gao, Wen,&Tian, Qi.(2019).GLAD: Global-Local-Alignment Descriptor for Scalable Person Re-Identification.IEEE TRANSACTIONS ON MULTIMEDIA,21(4),986-999.
MLA	Wei, Longhui,et al."GLAD: Global-Local-Alignment Descriptor for Scalable Person Re-Identification".IEEE TRANSACTIONS ON MULTIMEDIA 21.4(2019):986-999.