Safe Vision: Intelligent Surveillance for Public Safety Monitoring

Abstract

The "Crowd Monitoring" project focuses on enhancing public safety through real-time surveillance and detection systems. Utilizing advanced technologies, it provides crucial features like fall detection, criminal detection, fire detection, overcrowding alerts or weapons detection. These capabilities help identify potential hazards or suspicious behavior in crowded environments, enabling timely interventions. Built on the Django framework with Python 3.10, the system integrates machine learning models to process live video feeds, ensuring accurate detection and analysis. The front-end is developed using Bootstrap, offering an intuitive interface for users to monitor and manage alerts effectively. This system is designed to significantly improve situational awareness for security personnel in public spaces such as malls, stadiums, and transportation hubs. By continuously analyzing crowd behavior and detecting potential risks, the platform not only enhances safety but also reduces the response time to incidents. The integration of real-time notifications ensures that authorities can act quickly to mitigate threats, while the modular design allows for scalability and the addition of new features in the future. The combination of AI-powered detection and responsive user interface makes this project a robust solution for modern crowd management and safety enhancement.