A Deep and Structured Metric Learning Method for Robust Person Re-Identification
文献类型:期刊论文
作者 | Ren, Chuan-Xian1; Xu, Xiao-Lin2; Lei, Zhen3,4 |
刊名 | PATTERN RECOGNITION |
出版日期 | 2019-12-01 |
卷号 | 96页码:12 |
ISSN号 | 0031-3203 |
关键词 | Metric learning Feature extraction Deep neural networks Imbalance regularization Person re-identification |
DOI | 10.1016/j.patcog.2019.106995 |
通讯作者 | Ren, Chuan-Xian() |
英文摘要 | Person re-identification (re-ID) is to match different images of the same pedestrian. It has attracted increasing research interest in pattern recognition and machine learning. Traditionally, person re-ID is formulated as a metric learning problem with binary classification output. However, higher order relationship, such as triplet closeness among the instances, is ignored by such pair-wise based metric learning methods. Thus, the discriminative information hidden in these data is insufficiently explored. This paper proposes a new structured loss function to push the frontier of the person re-ID performance in realistic scenarios. The new loss function introduces two margin parameters. They operate as bounds to remove positive pairs of very small distances and negative pairs of large distances. A trade-off coefficient is assigned to the loss term of negative pairs to alleviate class-imbalance problem. By using a linear function with the margin-based objectives, the gradients w.r.t. weight matrices are no longer dependent on the iterative loss values in a multiplicative manner. This makes the weights update process robust to large iterative loss values. The new loss function is compatible with many deep learning architectures, thus, it induces new deep network with pair-pruning regularization for metric learning. To evaluate the performance of the proposed model, extensive experiments are conducted on benchmark datasets. The results indicate that the new loss together with the ResNet-50 backbone has excellent feature representation ability for person re-ID. (C) 2019 Elsevier Ltd. All rights reserved. |
资助项目 | National Natural Science Foundation of China[61572536] ; National Natural Science Foundation of China[11631015] ; National Natural Science Foundation of China[U1611265] ; Science and Technology Program of GuangZhou[201804010248] |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
出版者 | ELSEVIER SCI LTD |
WOS记录号 | WOS:000487569700033 |
资助机构 | National Natural Science Foundation of China ; Science and Technology Program of GuangZhou |
源URL | [http://ir.ia.ac.cn/handle/173211/26626] |
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 | Ren, Chuan-Xian |
作者单位 | 1.Sun Yat Sen Univ, Sch Math, Intelligent Data Ctr, Guangzhou 510275, Guangdong, Peoples R China 2.Guangdong Univ Finance & Econ, Sch Math & Stat, Guangzhou 510320, Guangdong, Peoples R China 3.Chinese Acad Sci, Inst Automat, Ctr Biometr & Secur Res, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Ren, Chuan-Xian,Xu, Xiao-Lin,Lei, Zhen. A Deep and Structured Metric Learning Method for Robust Person Re-Identification[J]. PATTERN RECOGNITION,2019,96:12. |
APA | Ren, Chuan-Xian,Xu, Xiao-Lin,&Lei, Zhen.(2019).A Deep and Structured Metric Learning Method for Robust Person Re-Identification.PATTERN RECOGNITION,96,12. |
MLA | Ren, Chuan-Xian,et al."A Deep and Structured Metric Learning Method for Robust Person Re-Identification".PATTERN RECOGNITION 96(2019):12. |
入库方式: OAI收割
来源:自动化研究所
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