Deep learning based vehicle violation detection system

Due to the limited road resources and the ever-increasing number of vehicles and persons, more frequent traffic violations and higher management costs have resulted. It is crucial to propose a more intelligent and less cost scheme to solve the traffic management problem. In this paper, we design and implement a vehicle violation detection system based on deep learning, which includes the detection, tracking and recognition of vehicles. On this basis, the detection and real-time alarm of common violations, such as red light running and impolite pedestrian, are also supported. Compared with the traditional detection and monitoring based on physical equipment, our system is completely based on computer vision, where the cutting-edge achievements of deep learning have been improved and applied. The system is not only more intelligent, but also can reduce the cost to a greater extent. Experiments illustrate that the system can meet the needs of the intelligent management of urban traffic through real-time monitoring and data analysis of the traffic scenes.