Crowd Management in Railway System Using Deep Learning

As urbanization progresses and greater demands for public security are imposed by busy surroundings like train stations, conventional surveillance systems become handicapped by core shortcomings in terms of responsiveness, scalability, and effectiveness. Most of these conventional systems are human-operated for real-time surveillance of CCTV live feeds, and it is prone to lag in response, to exhaustion, as well as the oversight of abnormalities owing to a voluminous stream of data. This project overcomes these challenges by taking advantage of Artificial Intelligence (AI) and Machine Learning (ML) to optimize real-time crowd management through the YOLOv5 object detection algorithm. The primary aim is to transform passive surveillance networks into proactive monitoring systems that can automatically detect crowd density, detect abnormal movement patterns, and trigger timely alerts without human intervention. YOLOv5 is chosen due to its high detection rates, speed, and light footprint, making it ideal for real-time video inspection on edge or low-power systems. The system inspects real-time video feeds from current CCTV equipment, identifies and tracks persons, and generates alerts upon threshold breaches of crowd levels or established behavioral abnormalities. The implementation of this AI model on existing surveillance arrangements is a valuable addition to the area of operational safety, being a cost-optimized, scale-up, technology-enabled solution towards crime prevention and crowd management. Not only is the system improved situational awareness but also eased the load of human resources with increased decision support through actionable insight. This study substantiates the efficiency of the designed solution via simulation and live trials, with significantly improved response times for threat detection, surveillance coverage, and general security performance. In addition, the modularity of the system makes it convenient to scale and adapt across environments such as transportation centers, arenas, and crowded public areas