“STOPEDE”-A STAMPEDE DETECTION SYSTEM

Large public gatherings such as festivals, religious events, concerts, and sports venues are increasingly vulnerable to crowd-related disasters, particularly stampedes caused by panic, congestion, or sudden disturbances.Early detection of abnormal crowd behavior is essential to prevent loss of life and ensure public safety.This paper presents STOPEDE, a real-time stampede detection system designed to identify early warning signs of dangerous crowd dynamics.The proposed system continuously monitors crowd movement patterns using video surveillance data and sensor-based inputs to analyze parameters such as crowd density, velocity, direction changes, and irregular motion.By combining computer vision techniques with intelligent decisionmaking algorithms, STOPEDE distinguishes between normal crowd flow and potentially hazardous situations.The system generates timely alerts for authorities when critical thresholds are exceeded, enabling rapid intervention and effective crowd management.Emphasis is placed on scalability, accuracy, and low latency to ensure reliable performance in large-scale environments.Experimental evaluations demonstrate that the system effectively detects high-risk crowd behavior while minimizing false alarms.STOPEDE aims to support emergency response teams and event organizers by providing an automated, proactive safety mechanism that reduces dependence on manual monitoring.The implementation of such a system can significantly enhance public safety and mitigate stampede-related disasters in densely populated scenarios.