CrowdShield Predicting Stampedes or Panic in Crowded Places via Video Analytics

Crowd management in large public gatherings is critical for preventing panic and stampede-related incidents. Traditional surveillance systems rely heavily on manual monitoring, making them inefficient for real-time risk detection. This paper introduces CrowdShield, a real-time crowd behavior monitoring system that leverages the Yolov11 object detection model to automatically identify behaviors indicative of potential danger, such as running, conflicting movement directions, and the presence of non-pedestrian obstacles. CrowdShield classifies human activities into categories including Running, Sitting, Standing, Opposite-Direction Movement, and Non-Pedestrian Entities, allowing early detection of panic-inducing patterns. The system supports both live video streams and uploaded images, providing instant visual feedback and alerts through an interactive interface. Experimental results demonstrate high detection accuracy and responsiveness, showcasing Yolov11’s effectiveness in complex, high-density environments. CrowdShield presents a scalable, automated, and proactive solution for enhancing public safety during large-scale events.

Keywords— Crowd behavior analysis, Yolov11, Real-time detection, Panic prevention, Stampede detection, Deep learning, Public safety, Object detection.