Real-Time Anomaly Detection and Crowd Safety Management System

Ensuring public safety in crowded environments such as stadiums, transportation hubs, and public events is
a critical challenge. This project presents a Real-Time Anomaly Detection and Crowd Safety Management System that
leverages artificial intelligence, computer vision, and data analytics to monitor and manage crowd behavior effectively.
The system utilizes advanced deep learning models to analyze live video feeds from surveillance cameras and detect
unusual or potentially dangerous activities such as overcrowding, sudden movements, violence, or unauthorized access.
By employing techniques like object detection, motion analysis, and pattern recognition, the system can identify
anomalies in real time with high accuracy. Additionally, the platform integrates alert mechanisms that notify authorities
instantly when suspicious activities are detected, enabling rapid response and prevention of potential hazards. It also
includes crowd density estimation and predictive analytics to anticipate risky situations before they escalate. To ensure
reliability and ethical deployment, the system incorporates data privacy, secure data handling, and controlled access
mechanisms. The solution is scalable and adaptable for use in smart cities, public gatherings, and emergency
management scenarios. Overall, this system demonstrates the potential of AI-driven technologies in enhancing crowd
monitoring, improving situational awareness, and ensuring public safety in real-time environments.