Crowd Shield AI Safety

Abstract

Crowd management and public safety have become critical concerns in densely populated environments such as railway stations, public events, and urban areas. This paper presents Crowd Shield AI Safety, an intelligent real-time crowd monitoring system that leverages deep learning techniques to detect crowd density and generate alerts to prevent overcrowding situations. The proposed system utilizes the ShanghaiTech dataset for training a Convolutional Neural Network (CNN) model to estimate crowd density from video streams. The system integrates real-time video processing, threshold-based detection, and automated alert mechanisms including voice alerts, email notifications, and a monitoring dashboard built using Streamlit. Experimental results demonstrate that the model effectively identifies overcrowded conditions and triggers alerts with high reliability. The proposed solution provides a scalable and efficient approach for improving public safety through automated surveillance. Future enhancements include mobile integration and real-time push notifications for smarter crowd management systems.