Potential Escalator-related Injury Identification and Prevention Based on Multi-module Integrated System for Public Health
This addresses public health risks from escalator injuries by providing real-time monitoring, though it is incremental as it applies existing computer vision methods to a specific safety domain.
The study tackled the problem of ongoing and impending escalator-related injuries by designing a computer vision-based multi-module safety monitoring system to detect unsafe behaviors like losing balance, not holding handrails, and carrying large items, with experimental results showing good performance and application potential.
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.