Abstract:
The increasing demand for mobility in urban areas has led to traffic congestion, which results in wastage of time and resources. The use of traffic surveillance systems, which employ computer vision and deep learning techniques, has the potential to regulate traffic flow and improve the efficiency of commutation. This project aims to develop a real-time surveillance system for automated citation and traffic regulation. The system includes modules for traffic surveillance, speed detection, challan generation, and traffic signal automation. The system can be used by traffic police departments to reduce workforce requirements, enforce traffic laws, and increase revenue earned from challans. Additionally, it can benefit the general public by increasing security and reducing time spent waiting at signals. The project uses dummy footage, databases, and excise servers to simulate the functioning of a real-world system. The project’s scope includes the development of a software application that can be implemented at every signal in urban areas. Overall, the project aims to address the challenges posed by traffic congestion in developing countries, while improving the physical and economic development of towns and cities
