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Deep Second-Order Siamese Network for Pedestrian Re-identification

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10112))

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Abstract

Typical pedestrian re-identification system consists of feature extraction and similarity learning modules. The learning methods involved in the two modules are usually designed separately, which makes them sub-optimal to each other, let alone to the re-identification target. In this paper, we propose a deep second-order siamese network for pedestrian re-identification which is composed of a deep convolutional neural network and a second-order similarity model. The deep convolutional network learns comprehensive features automatically from the data. The similarity model exploits second-order information, thus more suitable for re-identification setting than traditional metric learning methods. The two models are jointly trained over one unified large margin objective and the consistent convergence is guaranteed. Moreover, our deep model can be trained effectively with a small pedestrian re-identification dataset, through an irrelevant pre-training and relevant fine-tuning process. Experimental results on two public datasets illustrate the superior performance of our model over other state-of-the-art methods.

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Acknowledgment

This work was supported in part by National Basic Research Program of China (973 Program): 2015CB351802, and Natural Science Foundation of China (NSFC): 61272319, 61390515 and 61572465.

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Authors and Affiliations

  1. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China

    Xuesong Deng, Bingpeng Ma, Hong Chang, Shiguang Shan & Xilin Chen

  2. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China

    Xuesong Deng & Bingpeng Ma

Authors
  1. Xuesong Deng
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  2. Bingpeng Ma
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  3. Hong Chang
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  4. Shiguang Shan
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  5. Xilin Chen
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Corresponding author

Correspondence to Hong Chang .

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Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Deng, X., Ma, B., Chang, H., Shan, S., Chen, X. (2017). Deep Second-Order Siamese Network for Pedestrian Re-identification. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10112. Springer, Cham. https://doi.org/10.1007/978-3-319-54184-6_20

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  • DOI: https://doi.org/10.1007/978-3-319-54184-6_20

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-54184-6

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