Real-Time Queue Detection and Management System Using YOLO Object Detection

This study propounds a novel queue real-time de- tection and management system designed to aid public service delivery efficiency based on the YOLO object detection model. The proposed system takes in live feed from surveillance cameras, identifies and counts people waiting in lines, and gives real- time information through an easy-to-use web interface. Key issues include dealing with occlusion, responding to changing lighting conditions and facilitating scalability for different service environments. The proposed system highlights the introduction of advanced machine learning approaches as part of modern web technologies in order to optimize resource allocation, eliminate waiting, and enhance customer experience. This system is highly accurate and robustly performs in most real-world conditions and is applicable to hubs of transportation, shopping malls, or government offices.