Traffic Parameter Estimation System in Urban Scene Based on Machine Vision

The estimation and acquisition of traffic parameter information is the key to solving urban management and control problems. This paper proposed a novel video-based traffic parameter extraction system. In the first part, we used advanced techniques such as deep learning, calibration method, and image processing to obtain the key information such as vehicle trajectories of the traffic video. In the second part, all information of the first part was processed uniformly and generated traffic parameters such as traffic flow, vehicle type, vehicle composition of different vehicle types, and speed of vehicles passing through a scene in a traffic video. The results show that the accuracy of the information obtained by the proposed system can reach more than 90%. High-precision and abundant traffic parameters can provide important data support for traffic management and control, which illustrate the importance and significance of the proposed system.