[1]DING Yu,BI Xiaojun.Clothes-changing person re-identification by sample relationship optimization and re-ranking[J].CAAI Transactions on Intelligent Systems,2025,20(1):101-108.[doi:10.11992/tis.202404005]
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Clothes-changing person re-identification by sample relationship optimization and re-ranking

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 1 Page number: 101-108 Column: 学术论文—机器感知与模式识别 Public date: 2025-01-05
Title:
Clothes-changing person re-identification by sample relationship optimization and re-ranking
Author(s):
DING Yu1;  BI Xiaojun2; 3
1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Beijing 100081, China;
3. Department of Information Engineering, Minzu University of China, Beijing 100081, China
Keywords:
deep learning; clothes-changing person re-identification; local feature extraction; sample relationships optimization; Transformer; shortcut connections; circle loss; re-ranking
CLC:
TP391
DOI:
10.11992/tis.202404005
Abstract:
Current clothes-changing person re-identification models often have limited local feature extraction capabilities and insufficient sample relationship optimization. To this end, this paper proposes a novel clothes-changing person re-identification model by sample relationship optimization and re-ranking. Firstly, we design a Transformer model with shortcut connections to fuse initial and deep features, thereby optimizing the feature representation of each sample. Meanwhile, we use circle loss to assign different weights to sample pairs with varying optimization difficulties, which can better optimize the relationships among different samples. Finally, we designed a k′-reciprocal re-ranking strategy, which can re-rank the similarity rankings and further enhance the re-identification accuracy. Extensive experiments conducted on publicly available datasets LTCC and PRCC demonstrate that comparing with other advanced models, the proposed model achieves better re-identification effect.
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Last Update: 2025-01-05

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