Advanced Surveillance System for Public Safety

In recent years, the necessity for a sophisticated monitoring system to guarantee public safety has grown more apparent due to the quick urbanization and rising population density in public areas. Despite being widely utilized everywhere, current closed-circuit television (CCTV) systems frequently rely too much on human oversight for monitoring and responding and are unable to detect threats in real time. An advanced surveillance system is presented in this study with the goal of overcoming the drawbacks of conventional methods. Our system uses intelligent deep learning algorithms for crowd density estimation and management, immediate crime prevention, and sophisticated anomaly identification. This method reduces the need for human operators while increasing the speed and accuracy of threat identification, increasing the overall effectiveness of surveillance operations. By using real-world testing, we offer a thorough assessment of the system’s performance and show its benefits over competing systems. The study also looks at ways to expand the system’s capabilities in the future, such as better anomaly detection, privacy-preserving techniques, and the incorporation of Internet of Things (IoT) devices. This study advances the creation of AI-powered public safety solutions, providing a scalable and effective answer to today’s urban problems.