AI-Based Crowd Surveillance System

Abstract

Overcrowding in public places such as railway stations, shopping malls, classrooms, and large events poses serious safety risks including stampedes, health hazards, and challenges in emergency evacuation. Traditional manual surveillance methods are often inefficient and prone to human error, making real-time crowd monitoring difficult. This project proposes an AI-based real-time crowd surveillance system that uses computer vision and deep learning techniques to automatically detect and analyze crowd density through live video streams from surveillance cameras. The system applies image preprocessing and object detection models to accurately identify individuals and estimate crowd levels. When density exceeds predefined safety limits, instant alerts are generated to enable timely preventive action. The proposed solution enhances public safety, supports efficient crowd management, and provides a cost-effective and scalable approach suitable for deployment in transportation hubs, educational institutions, public gatherings, and smart city environments.