Dynamic Re-Weighting and Cross-Camera Learning for Unsupervised Person Re-Identification
文献类型：期刊论文
| 作者 | Yin, Qingze2; Wang, Guan'an1; Wu, Jinlin1 ; Luo, Haonan2; Tang, Zhenmin2
|
| 刊名 | MATHEMATICS
 |
| 出版日期 | 2022-05-01 |
| 卷号 | 10期号:10页码:17 |
| 关键词 | clustering dynamic re-weighting person attributes cross-camera triplet loss |
| DOI | 10.3390/math10101654 |
| 通讯作者 | Tang, Zhenmin(tzm.cs@njust.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | ATTRIBUTE ; FEATURES |
| WOS研究方向 | Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000803416800001 |
| 出版者 | MDPI |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49536]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心 |
| 通讯作者 | Tang, Zhenmin |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, 200 Xiaolingwei St, Nanjing 210094, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Qingze,Wang, Guan'an,Wu, Jinlin,et al. Dynamic Re-Weighting and Cross-Camera Learning for Unsupervised Person Re-Identification[J]. MATHEMATICS,2022,10(10):17. |
| APA | Yin, Qingze,Wang, Guan'an,Wu, Jinlin,Luo, Haonan,&Tang, Zhenmin.(2022).Dynamic Re-Weighting and Cross-Camera Learning for Unsupervised Person Re-Identification.MATHEMATICS,10(10),17. |
| MLA | Yin, Qingze,et al."Dynamic Re-Weighting and Cross-Camera Learning for Unsupervised Person Re-Identification".MATHEMATICS 10.10(2022):17. |
入库方式： OAI收割
来源：自动化研究所
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