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Potential escalator-related injury identification and prevention based on multi-module integrated system for public health

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Abstract

Escalator-related injuries threaten public health with the widespread use of escalators. The existing studies tend to focus on after-the-fact statistics, reflecting on the original design and use of defects to reduce the impact of escalator-related injuries, but few attention has been paid to ongoing and impending injuries. In this study, a multi-module escalator safety monitoring system based on computer vision is designed and proposed to simultaneously monitor and deal with three major injury triggers, including losing balance, not holding on to handrails and carrying large items. The escalator identification module is utilized to determine the escalator region, namely the region of interest. The passenger monitoring module is leveraged to estimate the passengers’ pose to recognize unsafe behaviors on the escalator. The dangerous object detection module detects large items that may enter the escalator and raises alarms. The processing results of the above three modules are summarized in the safety assessment module as the basis for the intelligent decision of the system. The experimental results demonstrate that the proposed system has good performance and great application potential.

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Acknowledgements

This work is supported by the financial support from GDAS’ Project of Science and Technology Development (Grant Nos. 2020GDASYL-20200302015, 2021GDASYL-20210103090) and Key-Area Research and Development Program of Guangdong Province (Grant No. 2018B010108006).

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Authors and Affiliations

  1. Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, China

    Zeyu Jiao, Huan Lei & Zhenyu Zhong

  2. China Aerospace Academy of Systems Science and Engineering, Beijing, China

    Hengshan Zong

  3. Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Yingjie Cai

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  1. Zeyu Jiao
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  2. Huan Lei
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  3. Hengshan Zong
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  4. Yingjie Cai
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Correspondence to Yingjie Cai.

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Jiao, Z., Lei, H., Zong, H. et al. Potential escalator-related injury identification and prevention based on multi-module integrated system for public health. Machine Vision and Applications 33, 29 (2022). https://doi.org/10.1007/s00138-022-01273-2

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