A benchmark for clothes variation in person re‐identification. Issue 12 (24th August 2020)

• Record Type: Journal Article
• Authors: Wang, Kai; Ma, Zhi; Chen, Shiyan; Yang, Jinni; Zhou, Keke; Li, Tao
• Is Part Of: International journal of intelligent systems. Volume 35:Issue 12(2020)
• Page Start: 1881
• Page End: 1898
• Publication Date: 2020-08-24
• Subjects: clothes variation -- deep learning -- metric learning -- person re‐identification; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22276
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: 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 https://github.com/nkicsl/NKUP-dataset .