A Computer Vision-Based Pedestrian Flow Management System for Footbridges and Its Applications

Urban footbridges are critical infrastructure increasingly challenged by vibration issues induced by crowd activity. Real-time monitoring of pedestrian dynamics is essential for evaluating structural safety, ensuring pedestrian comfort, and enabling proactive management. This paper proposes a lightweight, fully automated computer vision system for real-time monitoring of crowd dynamics on footbridges. The system integrates object detection, multi-target tracking, and monocular depth estimation to precisely quantify key crowd metrics: pedestrian flow rate, density, and velocity. Experimental validation demonstrated high performance: Flow rate estimation achieved 92.7% accuracy; density estimation yielded a 2.05% average relative error; and velocity estimation showed an 8.7% average relative error. Furthermore, the system demonstrates practical utility by successfully categorizing pedestrian behaviors using velocity data and triggering timely warnings. Crucially, field tests confirmed a minimum error of 5.56% between bridge vibration simulations driven by the system’s captured crowd data and physically measured acceleration data. This high agreement validates the system’s capability to provide reliable inputs for structural assessment. The proposed system establishes a practical technological foundation for intelligent footbridge management, focusing on safety, comfort, and operational efficiency through real-time crowd insights and automated alerts.