A benchmark for clothes variation in person re-identification

被引：30

作者：

Wang, Kai ^{[1
]}

Ma, Zhi ^{[1
]}

Chen, Shiyan ^{[1
]}

Yang, Jinni ^{[1
]}

Zhou, Keke ^{[1
]}

Li, Tao ^{[1
,2
]}

机构：

[1] Nankai Univ, Coll Comp Sci, Tianjin 300350, Peoples R China

[2] Nankai Univ, Tianjin Key Lab Network & Data Secur Technol, Tianjin, Peoples R China

来源：

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS | 2020年 / 35卷 / 12期

关键词：

clothes variation; deep learning; metric learning; person re-identification; ADAPTATION;

D O I：

10.1002/int.22276

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Person re-identification (re-ID) has drawn attention significantly in the computer vision society due to its application and research significance. It aims to retrieve a person of interest across different camera views. However, there are still several factors that hinder the applications of person re-ID. In fact, most common data sets either assume that pedestrians do not change their clothing across different camera views or are taken under constrained environments. Those constraints simplify the person re-ID task and contribute to early development of person re-ID, yet a person has a great possibility to change clothes in real life. To facilitate the research toward conquering those issues, this paper mainly introduces a new benchmark data set for person re-identification. To the best of our knowledge, this data set is currently the most diverse for person re-identification. It contains 107 persons with 9,738 images, captured in 15 indoor/outdoor scenes from September 2019 to December 2019, varying according to viewpoints, lighting, resolutions, human pose, seasons, backgrounds, and clothes especially. We hope that this benchmark data set will encourage further research on person re-identification with clothes variation. Moreover, we also perform extensive analyses on this data set using several state-of-the-art methods. Our dataset is available at .

引用

页码：1881 / 1898

页数：18

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