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Passenger Behavior Feature Identification in Urban Rail Station Surveillance Videos Using Skeleton Recognition Techniques
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Yang GUAN1, 2, Limin JIA1, Sihan TAO1, Fei DOU3
Urban Rapid Rail Transit | 2025, 38(1) : 106 - 111
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Urban Rapid Rail Transit | 2025, 38(1): 106-111
• Academic Discussion •
Passenger Behavior Feature Identification in Urban Rail Station Surveillance Videos Using Skeleton Recognition Techniques
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Yang GUAN1, 2, Limin JIA1, Sihan TAO1, Fei DOU3
Affiliations
  • 1 School of Traffic and Transportation Beijing Jiaotong University Beijing 100044
  • 2 CRSC Research & Design Institute Group Co., Ltd. Beijing 100070
  • 3 Beijing Union University Beijing 100101
doi: 10.3969/j.issn.1672-6073.2025.01.014
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In order to solve the problem that the traditional monitoring analysts in the field of urban rail transit have a high falsenegative rates and complex parameter adjustment of abnormal behaviors such as falling, fainting and fighting, making them difficult to apply efficiently to actual urban rail station monitoring scenarios, this paper proposes a human posture feature recognition framework based on skeleton pattern recognition, introducing the attitude estimation technology based on human skeleton. The Alpha Pose model is used to accurately estimate the posture of passengers, and combined with the Spatial Temporal Graph Convolutional Networks model, it achieves abnormal behavior recognition in the monitoring scenario of urban rail stations. By achieving 72.3 mAP on the COCO dataset and 82.1 mAP on the MPII dataset, the performance is improved by up to 17% compared to the OpenPose model, verifying the effectiveness and practicality of the model. The results show that the method proposed in this paper not only improves the recognition speed of passenger behavior but also has the ability to adapt to complex scenarios, providing a new technical solution for urban rail safety monitoring.

rail transit  /  skeleton recognition  /  pattern recognition  /  urban rail station safety  /  passenger behavior identification  /  spatial
Yang GUAN, Limin JIA, Sihan TAO, Fei DOU. Passenger Behavior Feature Identification in Urban Rail Station Surveillance Videos Using Skeleton Recognition Techniques[J]. Urban Rapid Rail Transit, 2025 , 38 (1) : 106 -111 . DOI: 10.3969/j.issn.1672-6073.2025.01.014
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Article Info
doi: 10.3969/j.issn.1672-6073.2025.01.014
  • Receive Date：2024-03-19
  • Online Date：2025-07-09
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  • Received：2024-03-19
  • Revised：2024-08-13
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Affiliations
    1 School of Traffic and Transportation Beijing Jiaotong University Beijing 100044
    2 CRSC Research & Design Institute Group Co., Ltd. Beijing 100070
    3 Beijing Union University Beijing 100101
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