Integrating Smart Technologies for Enhanced Population Density Monitoring and Traffic Control at Pilgrimage Sites

Pilgrimage sites frequently face the immense challenge of managing large crowds, particularly during key religious events where thousands of visitors may gather. Traditional crowd management and traffic control techniques often prove inadequate due to the dynamic and dense nature of these gatherings. This project proposes a real-time monitoring system using advanced computer vision technologies to enhance safety and optimize the visitor experience at pilgrimage sites. The primary objectives include real-time crowd monitoring to detect and track individual movements, traffic flow analysis to efficiently manage pedestrian paths and reduce bottlenecks, and the enhancement of safety measures by identifying emergency situations and abnormal behaviors that could escalate into dangerous scenarios. This capability is vital for analyzing surveillance camera footage using sophisticated computer vision algorithms, achieving an accuracy rate of 94%, with YOLOv3 providing 93% accuracy in estimating crowd density and movement patterns. The system also analyzes overall crowd flow, helping decisionmakers implement effective crowd management strategies such as opening additional entry/exit points or redirecting flows to less congested areas. Overall, this project aims to improve safety, efficiency, and the visitor experience at pilgrimage sites through innovations in smart technologies and computer vision.