@inproceedings{a842c2f7e1a149d5b1b02bfd014c46b4,
title = "Bayesian Personalized-Wardrobe Model (BP-WM) for Long-Term Person Re-Identification",
abstract = "Long-term surveillance applications often involve having to re-identify individuals over several days. The task is made even more challenging due to changes in appearance features such as clothing over a longitudinal time-span of days or longer. In this paper, we propose a novel approach called Bayesian Personalized-Wardrobe Model (BPWM) for long-term person re-identification (re-ID) by employing a Bayesian Personalized Ranking (BPR) for clothing features extracted from video sequences. In contrast to previous long-term person re-ID works, we exploit the fact that people typically choose their attire based on their personal preferences and that knowing a person's chosen wardrobe can be used as a soft-biometric to distinguish identities in the long-term. We evaluate the performance of our proposed BP-WM on the extended Indoor Long-term Re-identification Wardrobe (ILRW) dataset. Experimental results show that our method achieves state-of-the-art performance and that BP-WM can be used as a reliable soft-biometric for person re-identification.",
author = "Lee, \{Kyung Won\} and Nishant Sankaran and Deen Mohan and Kenny Davila and Dennis Fedorishin and Srirangaraj Setlur and Venu Govindaraju",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 17th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2021 ; Conference date: 16-11-2021 Through 19-11-2021",
year = "2021",
doi = "10.1109/AVSS52988.2021.9663830",
language = "English",
series = "AVSS 2021 - 17th IEEE International Conference on Advanced Video and Signal-Based Surveillance",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "AVSS 2021 - 17th IEEE International Conference on Advanced Video and Signal-Based Surveillance",
address = "United States",
}