Dense Invariant Feature Based Support Vector Ranking for Cross-Camera Person Re-identification

Shoubiao Tan, Feng Zheng, Li Liu, Jungong Han, Ling Shao

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
4 Downloads (Pure)

Abstract

Recently, support vector ranking has been adopted to address the challenging person re-identification problem. However, the ranking model based on ordinary global features cannot well represent the significant variation of pose and viewpoint across camera views. To address this issue, a novel ranking method which fuses the dense invariant features is proposed in this paper to model the variation of images across camera views. An optimal space for ranking is learned by simultaneously maximizing the margin and minimizing the error on the fused features. The proposed method significantly outperforms the original support vector ranking algorithm due to the invariance of the dense invariant features, the fusion of the bidirectional features and the adaptive adjustment of parameters. Experimental results demonstrate that the proposed method is competitive with state-of-the-art methods on two challenging datasets, showing its potential for real-world person re-identification.
Original languageEnglish
Pages (from-to)356-363
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume28
Issue number2
Early online date21 Apr 2016
DOIs
Publication statusPublished - Feb 2018

Keywords

  • Person Re-identification
  • Support Vector Ranking (SVR)
  • Dense Invariant Feature (DIF)
  • Feature Fusion

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