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

文献类型：期刊论文

作者Wei, Longhui1; Zhang, Shiliang1; Yao, Hantao2; Gao, Wen1; Tian, Qi3,4
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2019-04-01
卷号21期号:4页码:986-999
关键词Person re-identification (Re-ID) global-local-alignment descriptor retrieval framework
ISSN号1520-9210
DOI10.1109/TMM.2018.2870522
通讯作者Zhang, Shiliang(slzhang.jdl@pku.edu.cn)
英文摘要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.
WOS关键词IDENTIFICATION ; PERFORMANCE
资助项目NVIDIA NVAIL program
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000462413700014
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构NVIDIA NVAIL program
源URL[http://ir.ia.ac.cn/handle/173211/23498]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
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.

入库方式： OAI收割

来源：自动化研究所

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