Inter-camera Identity Discrimination for Unsupervised Person Re-identification
Authors: 
Mingfu Xiong, Kaikang Hu, Zhihan Lyu, Fei Fang, Zhongyuan Wang, Ruimin Hu, Khan MuhammadAuthors Info & Claims
Mingfu Xiong, Kaikang Hu, Zhihan Lyu, Fei Fang, Zhongyuan Wang, Ruimin Hu, Khan MuhammadAuthors Info & ClaimsArticle No.: 232, Pages 1 - 18
Abstract
Unsupervised person re-identification (Re-ID) has garnered significant attention because of its data-friendly nature, as it does not require labeled data. Existing approaches primarily address this challenge by employing feature-clustering techniques to generate pseudo-labels. In addition, camera-proxy-based methods have emerged because of their impressive ability to cluster sample identities. However, these methods often blur the distinctions between individuals within inter-camera views, which is crucial for effective person re-ID. To address this issue, this study introduces an inter-camera-identity-difference-based contrastive learning framework for unsupervised person Re-ID. The proposed framework comprises two key components: (1) a different sample cross-view close-range penalty module and (2) the same sample cross-view long-range constraint module. The former aims at penalizing excessive similarity among different subjects across inter-camera views, whereas the latter mitigates the challenge of excessive dissimilarity among the same subject across camera views. To validate the performance of our method, we conducted extensive experiments on three existing person Re-ID datasets (Market-1501, MSMT17, and PersonX). The results demonstrate the effectiveness of the proposed method, which shows a promising performance. The code is available at https://github.com/hooldylan/IIDCL.
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Index Terms
- Inter-camera Identity Discrimination for Unsupervised Person Re-identification
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August 2024
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Publication History
Published: 13 June 2024
Online AM: 03 April 2024
Accepted: 09 March 2024
Revised: 29 January 2024
Received: 07 November 2023
Published in TOMM Volume 20, Issue 8
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