This project aims to enhance public safety management by implementing intelligent technology for real-time monitoring, early warning, and prediction of passenger flow in specific scenarios such as metro station entrances and escalator openings. The project focuses on real-time passenger flow statistics at entrances and exits, early warning for passenger flow exceeding thresholds, and prediction of passenger flow during peak hours. By integrating these functionalities, the system comprehensively grasps passenger flow dynamics, promptly identifies potential risks, and provides scientific evidence for crowd guidance and safety management. The system employs object segmentation technology from images/videos to accurately identify and count pedestrians in videos; motion target tracking technology to monitor dynamic changes in passenger flow in real-time; and regression analysis based on statistical passenger flow data to predict peak-hour passenger flow. These technologies ensure the system's efficiency and accuracy in complex crowd movement scenarios. Real-time monitoring and intelligent analysis enable timely reporting of passenger flow exceeding thresholds to customer service management platforms, helping managers quickly take action to prevent accidents such as stampedes. The system not only improves the efficiency of passenger flow management but also provides a strong guarantee for public safety, offering significant application value and prospects for promotion.