IEEE Access (Jan 2019)

Contribution-Based Multi-Stream Feature Distance Fusion Method With <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-Distribution Re-Ranking for Person Re-Identification

  • Zhiyong Huang,
  • Zhi Yu,
  • Yinsong Li,
  • Yujie Wang,
  • Shuang Lin,
  • Daming Sun,
  • Yuanhong Zhong,
  • Hailin Cao,
  • Hans Gregersen

DOI
https://doi.org/10.1109/ACCESS.2019.2904278
Journal volume & issue
Vol. 7
pp. 35631 – 35644

Abstract

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Person re-identification (re-ID) is an important and challenging topic in video surveillance and public security. Re-ID aims to retrieve persons from different cameras. Despite the developments in recent years, re-ID still faces many challenges due to different camera views, changeable person posture, complex background, and occlusion. To exploit more discriminative image similarity descriptor, we propose a novel method in this paper. First, we design a body partition extraction network to extract three body regions with efficient alignment. Second, we propose a multi-stream contribution framework to fuse feature distance with different contributions and generate the final image similarity descriptor. In addition, we combine re-ID and semantic segmentation. A mask feature is introduced to the proposed framework and we design a contribution feedback module to generate contribution coefficients dynamically. Third, in order to improve re-ID performance, we propose a fragment learning method to optimize the contribution feedback module. Fourth and last, we propose a k-distribution re-ranking strategy to further improve performance. Our method achieves competitive results on two popular datasets, CUHK03, and Market1501, with rank-1 accuracy of 93.5% and 85.7%. The proposed re-ranking method achieves 2.3% and 2.8% performance boost. The data demonstrate the effectiveness of the proposed multi-stream contribution framework and the k-distribution re-ranking strategy.

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