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Person Re-identification with Patch-Based Local Sparse Matching and Metric Learning

  • Bo JiangEmail author
  • Yibing Lv
  • Aihua Zheng
  • Bin Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11902)

Abstract

Recently, patch based matching has been demonstrated effectively to address the spatial misalignment issue caused by camera-view changes or human pose variations in person re-identification (Re-ID) problem. In this paper, we propose a novel local sparse matching model to obtain a reliable patch-wise matching for Re-ID problem. In particular, in the training phase, we develop a robust Local Sparse Matching model to learn more precise corresponding relationship between patches of positive sample image pairs. In the testing phase, we adopt a local-global distance metric learning for Re-ID task by considering global and local information simultaneously. Extensive experiments on four benchmarks demonstrate the effectiveness of our approach.

Keywords

Person re-identification Graph matching Metric learning 

Notes

Acknowledgment

This work was supported by the National Natural Science Foundation of China (61602001), Natural Science Foundation of Anhui Province (1708085QF139), Open Project Program of the National Laboratory of Pattern Recognition (NLPR) (201900046).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyAnhui UniversityHefeiChina

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