A robust vehicle queuing and dissipation detection method based on two cameras

This paper proposed a robust framework of detecting the real-time queuing and dissipation of a vehicle queue by two cameras, one fixed at the front of the stop line and the other somewhere behind the stop line, jointly monitoring the interested region with opposite and long-range views. Firstly, the position changes of the tail and head of a vehicle queue, which accurately describe the formation and dissipation of the queue, can be efficiently tracked in each camera at intersection during morning and evening rush hours, with a duplex flexible window fused with the Haar feature based AdaBoost cascade classifiers. Secondly, the data of these two cameras in this large-area outdoor traffic application are fused at decision level to improve the accuracy of the tracking, according to the tracking result in each camera. Then, the queue length and stop delay of vehicles can be calculated readily. Experiments show that the proposed method can detect the formation and dissipation of the queue under varying illumination in real time, and that the accuracy rate is about 90.24%. Therefore, this method can be further applied to traffic congestion monitoring and traffic signal controlling.