Published Paper Details:

The goal of the proposed project is to improve public safety by developing and deploying a sophisticated cloud-based surveillance system that tracks pedestrians in real time and detects crimes using YOLOv8 deep learning. The foundation, known for its exceptional object detection precision, is YOLOv8, which guarantees precise object identification in security video. The technology allows for the easy integration of several cameras and is effortlessly integrated into a scalable cloud infrastructure. Using state-of-the-art deep learning techniques along with web and mobile applications for quick alerts and real-time monitoring, the system provides a full solution. Law enforcement and security workers may stay in touch and respond to emergencies from any place thanks to this connection. Because of the user-friendly interface offered by the web and mobile apps, authorities can effectively visualize and evaluate the surveillance data. One important aspect of the cloud architecture is its scalability, which allows for the integration of an expanding network of security cameras as needed. This guarantees the system's flexibility and future-proofing to satisfy the changing needs of public safety. Strengthening the proactive capacities of law enforcement through