CASIA OpenIR  > 智能制造技术与系统研究中心  > 多维数据分析
Region-specific Metric Learning for Person Re-identification
Min Cao1,2; Chen Chen1,2; Xiyuan Hu1,2; Silong Peng1,2,3
2018
Conference NameInternational Conference on Pattern Recognition (ICPR)
Conference Date2018.08.20-24
Conference PlaceBeijing, China
Abstract

Person re-identification addresses the problem of matching individual images of the same person captured by different non-overlapping camera views. Distance metric learning plays an effective role in addressing the problem. With the features extracted on several regions of person image, most of distance metric learning methods have been developed in which the learnt cross-view transformations are region-generic, i.e all region-features share a homogeneous transformation. The spatial structure of person image is ignored and the distribution difference among different region-features is neglected. Therefore in this paper, we propose a novel region-specific metric learning method in which a series of region-specific sub-models are optimized for learning cross-view region-specific transformations. Additionally, we also present a novel feature pre-processing scheme that is designed to improve the features' discriminative power by removing weakly discriminative features. Experimental results on the publicly available VIPeR, PRID450S and QMUL GRID datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25784
Collection智能制造技术与系统研究中心_多维数据分析
Corresponding AuthorChen Chen
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.Beijing ViSystem Corporation Limited, China
3.University of Chinese Academy of Sciences, Beijing, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Min Cao,Chen Chen,Xiyuan Hu,et al. Region-specific Metric Learning for Person Re-identification[C],2018.
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