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Research on Multi-scale Pedestrian Attribute Recognition Based on Dual Self-attention Mechanism

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Mobile Networks and Management (MONAMI 2023)

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Abstract

As one of the important fields of computer vision research, pedestrian attribute recognition has gained increasing attention from domestic and foreign researchers due to its huge potential applications. However, obtaining long-distance pedestrian information in actual scenes poses challenges such as lack of information, incomplete feature extraction, and low attribute recognition accuracy. To address these issues, we propose a multi-scale feature fusion network based on a dual self-attention mechanism. The fusion module merges multi-scale features to enable more complete attribute extraction, while the dual self-attention module focuses the network on important regions. Experimental results on PA-100K, RAP, and PETA datasets achieved mean accuracies of 81.97%, 81.53%, and 86.37%, respectively. Extensive experiments demonstrate that the proposed method is highly competitive in pedestrian attribute recognition.

The Jiangxi Province Office of Education provided funding support for this research.

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Acknowledgment

This research received partial support from the National Natural Science Foundation of China(No. 62067003) and the Foundation of Jiangxi Educational Committee (No. GJJ200824).

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Authors and Affiliations

  1. School of Software Engineering, Jiangxi University of Science and Technology, NanChang, 330013, Jiangxi, People’s Republic of China

    He Xiao, Wenbiao Xie, Ruoni Zhang & Xiao Xu

  2. Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications, Nanchang, 330013, People’s Republic of China

    He Xiao

  3. Information and Communication Branch, State Grid Jiangxi Electric Power Co, Nanchang, 330095, China

    Yang Zhou

  4. School of Software, Jiangxi Normal University, Nanchang, 330022, China

    Yong Luo

Authors
  1. He Xiao
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  2. Wenbiao Xie
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  3. Yang Zhou
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  4. Yong Luo
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  5. Ruoni Zhang
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  6. Xiao Xu
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Corresponding author

Correspondence to He Xiao .

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Editors and Affiliations

  1. The University of Electro-Communications, Tokyo, Japan

    Celimuge Wu

  2. VTT Technical Research Centre, Helsinki, Finland

    Xianfu Chen

  3. Xidian University, Xi'an, China

    Jie Feng

  4. Jiangxi University of Technology, Nanchang, China

    Zhen Wu

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Xiao, H., Xie, W., Zhou, Y., Luo, Y., Zhang, R., Xu, X. (2024). Research on Multi-scale Pedestrian Attribute Recognition Based on Dual Self-attention Mechanism. In: Wu, C., Chen, X., Feng, J., Wu, Z. (eds) Mobile Networks and Management. MONAMI 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-55471-1_16

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  • DOI: https://doi.org/10.1007/978-3-031-55471-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-55470-4

  • Online ISBN: 978-3-031-55471-1

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