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Salient Foreground-Aware Network for Person Search

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Book cover Biometric Recognition (CCBR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13628))

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Abstract

Person search aims to simultaneously localize and identify a query person from realistic and uncropped images, which consists of person detection and re-identification. In existing methods, the extracted features come from the low-quality proposals generated by the structure like Region Proposal Network (RPN), and convolution is used to learn local features in the process of extracting features while the receptive field cannot grasp the global structural information. We propose an end-to-end network embedded with our Salient Foreground-Aware Module (SFAM). Self-attention mechanism in SFAM allows for better capture of global information. Our network incorporates our embedding method for person detection and person re-identification, which can effectively optimize the process of extracting features and improve feature expression capabilities. We merge the above modules into our Salient Foreground-Aware Network (SFAN). Extensive experiments have shown that our SFAN significantly improves the performance of end-to-end models with acceptable time-consuming and achieves state-of-the-art results.

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Acknowledgment

This project was supported by the NSFC 62076258.

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Authors and Affiliations

  1. The School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510006, China

    Hongxu Chen, Quan Zhang & Jianhuang Lai

  2. The Guangdong Key Laboratory of Information Security Technology, Guangzhou, 510006, China

    Hongxu Chen, Quan Zhang & Jianhuang Lai

  3. The Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education China, Guangzhou, 510006, China

    Jianhuang Lai

Authors
  1. Hongxu Chen
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  2. Quan Zhang
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  3. Jianhuang Lai
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Corresponding author

Correspondence to Jianhuang Lai .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Weihong Deng

  2. Tsinghua University, Beijing, China

    Jianjiang Feng

  3. Beihang University, Beijing, China

    Di Huang

  4. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Meina Kan

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  6. Tsinghua University, Beijing, China

    Fang Zheng

  7. China Electronics Standardization Institute, Beijing, China

    Wenfeng Wang

  8. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaofeng He

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Chen, H., Zhang, Q., Lai, J. (2022). Salient Foreground-Aware Network for Person Search. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_44

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  • DOI: https://doi.org/10.1007/978-3-031-20233-9_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20232-2

  • Online ISBN: 978-3-031-20233-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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