Tunnel Traffic Enforcement Using Visual Computing and Field-Programmable Gate Array-Based Vehicle Detection and Tracking

Tunnels are commonly found in small and enclosed environments on highways, roads, or city streets. They are constructed to pass through mountains or beneath crowded urban areas. To prevent accidents in these confined environments, lane changes, slow driving, or speeding are prohibited on single- or multi-lane one-way roads. We developed a foreground detection algorithm based on the K-nearest neighbor (KNN) and Gaussian mixture model and 400 collected images. The KNN was used to gather the first 200 image data, which were processed to remove differences and estimate a high-quality background. Once the background was obtained, new images were extracted without the background image to extract the vehicle’s foreground. The background image was processed using Canny edge detection and the Hough transform to calculate road lines. At the same time, the oriented FAST and rotated BRIEF (ORB) algorithm was employed to track vehicles in the foreground image and determine positions and lane deviations. This method enables the calculation of traffic flow and abnormal movements. We accelerated image processing using xfOpenCV on the PYNQ-Z2 and FPGA Xilinx platforms. The developed algorithm does not require pre-labeled training models and can be used during the daytime to automatically collect the required footage. For real-time monitoring, the proposed algorithm increases the computation speed ten times compared with YOLO-v2-tiny. Additionally, it uses less than 1% of YOLO’s storage space. The proposed algorithm operates stably on the PYNQ-Z2 platform with existing surveillance cameras, without additional hardware setup. These advantages make the system more appropriate for smart traffic management than the existing framework.