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Network Improved by Auxiliary Part Features for Person Re-identification

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Cognitive Systems and Signal Processing (ICCSIP 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1006))

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Abstract

Person re-identification (ReID) is an important issue in computer vision area. It focuses on identifying people under different scenarios. In this paper, we test the contributions of local part features in ReID system. With the auxiliary of local part features, our model achieves significantly improvements, which achieves rank-1 accuracy of 91.7% on market1501 dataset and 82.6% on MARS dataset. We also test the feasibility of using densenet as backbone model in ReID system. With densenet as our backbone model, our method achieves state-of-art performance and simultaneously reduces the model size enormously.

This work is supported in part by the Youth Innovation Foundation of the 4th China High Resolution Earth Observation Conference under Grant GFZX04061502.

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Author information

Authors and Affiliations

  1. Institute of Software Chinese Academy of Sciences, No. 4 Zhongguancun South Road, Haidian District, Beijing, China

    Zhongji Liu, Hui Zhang, Rui Wang, Haichang Li & Xiaohui Hu

Authors
  1. Zhongji Liu
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  2. Hui Zhang
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  3. Rui Wang
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  4. Haichang Li
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  5. Xiaohui Hu
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Corresponding authors

Correspondence to Zhongji Liu , Hui Zhang , Rui Wang , Haichang Li or Xiaohui Hu .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Fuchun Sun

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Huaping Liu

  3. College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China

    Dewen Hu

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Liu, Z., Zhang, H., Wang, R., Li, H., Hu, X. (2019). Network Improved by Auxiliary Part Features for Person Re-identification. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-7986-4_20

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  • DOI: https://doi.org/10.1007/978-981-13-7986-4_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7985-7

  • Online ISBN: 978-981-13-7986-4

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