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Airport Boarding Bridge Pedestrian Detection Based on Spatial Attention and Joint Crowd Density Estimation

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Artificial Intelligence (CICAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14474))

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Abstract

Pedestrian detection serves as the cornerstone of pedestrian tracking and re-identification, playing a pivotal role in the realm of intelligent transportation. Accurate identification of pedestrians with diverse identities, such as passengers, crew members, and cleaning staff, is of utmost importance in high-security-demand scenarios like airport boarding bridges. The varied poses of pedestrians, occlusions, and small appearance differences pose significant challenges for accurately detecting individuals with different identities in boarding bridge scenarios. Existing object detectors exhibit limited prowess in extracting discriminative features tailored specifically for pedestrians, hampering their ability to fulfill the requirements of precise localization and classification. In this paper, we propose a method based on spatial attention and joint crowd density estimation. By incorporating spatial attention, our network selectively focuses on salient regions corresponding to different pedestrian categories, thereby enhancing classification accuracy. Moreover, through introducing an auxiliary task of crowd density estimation, the supervision of pedestrian head position information is added to the network. This significantly alleviates the missed detection problems caused by perspective distortion and occlusion, leading to significant improvements in detection accuracy. In our study, we use YOLO as the baseline model. The improved model shows a 5.81% increase in mAP and significantly outperforms several common object detectors.

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 61976095 and in part by the Natural Science Foundation of Guangdong Province, China, under Grant 2022A1515010114.

Author information

Authors and Affiliations

  1. School of Automation Science and Engineering, South China University of Technology, Guangzhou, China

    Xu Han, Hao Wan, Wenxiao Tang & Wenxiong Kang

  2. Guangdong Airport Baiyun Information Technology Co., Ltd. Postdoctoral Innovation Practice Base, Guangzhou, China

    Hao Wan

  3. Pazhou Lab, Guangzhou, China

    Wenxiong Kang

  4. Guangdong Enterprise Key Laboratory of Intelligent Finance, Guangzhou, China

    Wenxiong Kang

Authors
  1. Xu Han
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  3. Wenxiao Tang
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Corresponding author

Correspondence to Wenxiong Kang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Duke University, Durham, NC, USA

    Jian Pei

  3. Shanghai Jiao Tong Univeristy, Shanghai, China

    Guangtao Zhai

  4. Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

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Han, X., Wan, H., Tang, W., Kang, W. (2024). Airport Boarding Bridge Pedestrian Detection Based on Spatial Attention and Joint Crowd Density Estimation. In: Fang, L., Pei, J., Zhai, G., Wang, R. (eds) Artificial Intelligence. CICAI 2023. Lecture Notes in Computer Science(), vol 14474. Springer, Singapore. https://doi.org/10.1007/978-981-99-9119-8_20

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  • DOI: https://doi.org/10.1007/978-981-99-9119-8_20

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  • Print ISBN: 978-981-99-9118-1

  • Online ISBN: 978-981-99-9119-8

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