A Survey of Person Re-identification - 文章详情页

作者：

Li, You-Jiao (Li, You-Jiao.) | Zhuo, Li (Zhuo, Li.) | Zhang, Jing (Zhang, Jing.) | Li, Jia-Feng (Li, Jia-Feng.) | Zhang, Hui (Zhang, Hui.)

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摘要：

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.　Copyright　©　2018　Acta　Automatica　Sinica.　All　rights　reserved.

关键词：

Learning systems Palmprint recognition Security systems Surveys Large dataset Deep learning Image processing Image resolution

作者机构：

[ 1 ] [Li, You-Jiao]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Li, You-Jiao]College of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Li, You-Jiao]College of Computer Science and Technology, Shandong University of Technology, Zibo; 255000, China
[ 4 ] [Zhuo, Li]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 5 ] [Zhuo, Li]College of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Zhuo, Li]Beijing Collaborative Innovation Center of Electric Vehicles, Beijing; 100081, China
[ 7 ] [Zhang, Jing]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 8 ] [Zhang, Jing]College of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 9 ] [Li, Jia-Feng]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 10 ] [Li, Jia-Feng]College of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 11 ] [Zhang, Hui]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 12 ] [Zhang, Hui]College of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China

通讯作者信息：

[zhuo, li]beijing collaborative innovation center of electric vehicles, beijing; 100081, china;;[zhuo, li]college of microelectronics, faculty of information technology, beijing university of technology, beijing; 100124, china;;[zhuo, li]beijing key laboratory of computational intelligence and intelligent system, beijing university of technology, beijing; 100124, china

电子邮件地址：

zhuoli@bjut.edu.cn

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