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Generating Pedestrian Images for Person Re-identification

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Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

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Abstract

Person re-identification (re-ID) is mainly used to search the target pedestrian in different cameras. In this paper, we employ generative adversarial network (GAN) to expand training samples and evaluate the performance of two different label assignment strategies for the generated samples. We also investigate how the number of generated samples influences the re-ID performance. We do several experiments on the Market1501 database, and the experimental results are of essential reference value to this research field.

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Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant No. 61501327 and No. 61711530240, Natural Science Foundation of Tianjin under Grant No. 17JCZDJC30600 and No. 15JCQNJC01700, the Fund of Tianjin Normal University under Grant No.135202RC1703, the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 201700001 and No. 201800002, the China Scholarship Council No. 201708120039 and No. 201708120040, and the Tianjin Higher Education Creative Team Funds Program.

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Authors and Affiliations

  1. Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, China

    Zhong Zhang, Tongzhen Si & Shuang Liu

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Zhong Zhang, Tongzhen Si & Shuang Liu

Authors
  1. Zhong Zhang
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  2. Tongzhen Si
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  3. Shuang Liu
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Corresponding author

Correspondence to Zhong Zhang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Zhang, Z., Si, T., Liu, S. (2020). Generating Pedestrian Images for Person Re-identification. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_5

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  • DOI: https://doi.org/10.1007/978-981-13-6504-1_5

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  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

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