Video Image Vehicle Detection System for Signaled Traffic Intersection

In modern intelligent transportation systems, the video image vehicle detection system (VIVDS) is gradually becoming one of the popular methods at signalized traffic intersection due to its convenient installation and rich information content provided. However, in the current VIVDS, the camera usually is installed at the roadside poles or traffic light poles, which not only requires more than one camera to cover the entire intersection, but also results in serious vehicle occlusions and adverse affects on the performance of the vehicle detection and tracking. Meanwhile, it is noted that the detection rate of the black, gray and dark color vehicles (such as red, blue, and green vehicles) are poor or incomplete detection by using the traditional background subtraction method in the RGB color model. To tackle these problems, this paper presents a novel VIVDS with the new camera installation, which only uses a single camera to cover the panorama view of the interested intersection. Furthermore, a robust vehicle detection algorithm with multi-information fusion has been developed to resolve problems of detecting incompletion, which plays a key role in enhancing the vehicle detection rate in the proposed VIVDS for urban traffic surveillance. The proposed system has been tested on a traffic image sequences recorded at typical urban intersections. The experimental results show that the system offers the flexibility to detect the different color vehicles, the robustness to noise and the efficiency of computation.