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Center-Level Verification Model for Person Re-identification

  • Ruochen Zheng
  • Yang Chen
  • Changqian Yu
  • Chuchu Han
  • Changxin GaoEmail author
  • Nong Sang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11256)

Abstract

In past years, convolutional neural network is increasingly used in person re-identification due to its promising performance. Especially, the siamese network has been widely used with the combination of verification loss and identification loss. However, the loss functions are based on the individual samples, which cannot represent the distribution of the identity in the scenario of deep learning. In this paper, we introduce a novel center-level verification (CLEVER) model for the siamese network, which simply represents the distribution as a center and calculates the loss based on the center. To simultaneously consider both intra-class and inter-class variation, we propose an intra-center submodel and an inter-center submodel respectively. The loss of CLEVER model, combined with identification loss and verification loss, is used to train the deep network, which gets state-of-the-art results on CUHK03, CUHK01 and VIPeR datasets.

Keywords

Center-level Intra-class variation Inter-class distance 

Notes

Acknowledgements

This work was supported by National Key R&D Program of China (No. 2018YFB1004600), the Project of the National Natural Science Foundation of China (No. 61876210), and Natural Science Foundation of Hubei Province (No. 2018CFB426).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ruochen Zheng
    • 1
  • Yang Chen
    • 1
  • Changqian Yu
    • 1
  • Chuchu Han
    • 1
  • Changxin Gao
    • 1
    Email author
  • Nong Sang
    • 1
  1. 1.Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of AutomationHuazhong University of Science and TechnologyWuhanChina

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