Dynamic Re-Weighting and Cross-Camera Learning for Unsupervised Person Re-Identification

Qingze Yin, Guan’an Wang, Jinlin Wu, Haonan Luo and Zhenmin Tang
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Qingze Yin: School of Computer Science and Engineering, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing 210094, China
Guan’an Wang: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Jinlin Wu: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Haonan Luo: School of Computer Science and Engineering, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing 210094, China
Zhenmin Tang: School of Computer Science and Engineering, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing 210094, China

Mathematics, 2022, vol. 10, issue 10, 1-17

Abstract: Person Re-Identification (ReID) has witnessed tremendous improvements with the help of deep convolutional neural networks (CNN). Nevertheless, because different fields have their characteristics, most existing methods encounter the problem of poor generalization ability to invisible people. To address this problem, based on the relationship between the temporal and camera position, we propose a robust and effective training strategy named temporal smoothing dynamic re-weighting and cross-camera learning (TSDRC). It uses robust and effective algorithms to transfer valuable knowledge of existing labeled source domains to unlabeled target domains. In the target domain training stage, TSDRC iteratively clusters the samples into several centers and dynamically re-weights unlabeled samples from each center with a temporal smoothing score. Then, cross-camera triplet loss is proposed to fine-tune the source domain model. Particularly, to improve the discernibility of CNN models in the source domain, generally shared person attributes and margin-based softmax loss are adapted to train the source model. In terms of the unlabeled target domain, the samples are clustered into several centers iteratively and the unlabeled samples are dynamically re-weighted from each center. Then, cross-camera triplet loss is proposed to fine-tune the source domain model. Comprehensive experiments on the Market-1501 and DukeMTMC-reID datasets demonstrate that the proposed method vastly improves the performance of unsupervised domain adaptability.

Keywords: clustering; dynamic re-weighting; person attributes; cross-camera triplet loss (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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