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Partially Separated Networks for Person Search

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Advances in Multimedia Information Processing – PCM 2018 (PCM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11166))

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Abstract

The multi-task learning framework that considers pedestrian detection and person re-identification jointly is an effective solution for person search. However, the existing joint frameworks simply share the backbone network without considering the negative interaction between the two tasks. To alleviate this conflict and meet the different requirements in detection and re-identification, a Partially Separated Network (PSN) for person search is proposed in this paper. Unlike the traditional joint frameworks, our backbone network is partially separated for detection and identification, and feature maps with different scales are provided according to different characteristics. Our experiment results have demonstrated that on CUHK-SYSU dataset our mAP and top-1 on ResNet-50 are 5.4% and 4.4% higher, and on PRW dataset our mAP and top-1 on PVANet are 8.0% and 5.0% higher compared with the state-of-the-art methods. Specially, the improvements can be more impressive in the case of large gallery, occlusion and low resolution.

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Acknowledgement

This work is supported by the Shenzhen Municipal Development and Reform Commission (Disciplinary Development Program for Date Science and Intelligent Computing), and by Shenzhen International cooperative research projects GJHZ20170313150021171.

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Authors and Affiliations

  1. Communication and Information Security Lab, Shenzhen Graduate School, Peking University, Shenzhen, China

    Chuanchuan Chen, Jingbo Fan, Yuesheng Zhu & Guibo Luo

Authors
  1. Chuanchuan Chen
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  2. Jingbo Fan
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  3. Yuesheng Zhu
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  4. Guibo Luo
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Corresponding author

Correspondence to Yuesheng Zhu .

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Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Richang Hong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. University of Tokyo, Tokyo, Japan

    Toshihiko Yamasaki

  4. Hefei University of Technology, Hefei, China

    Meng Wang

  5. City University of Hong Kong, Hong Kong, Hong Kong

    Chong-Wah Ngo

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Chen, C., Fan, J., Zhu, Y., Luo, G. (2018). Partially Separated Networks for Person Search. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_71

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  • DOI: https://doi.org/10.1007/978-3-030-00764-5_71

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00763-8

  • Online ISBN: 978-3-030-00764-5

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