A lane marking detection and tracking algorithm based on sub-regions

In this paper, we present a lane marking detection and tracking method for the Lane Departure Warning system (LDWs). The method can both detect straight and curve lanes on the highway and urban road. The lane marking detection part is divided into three phases. First, bisect the ROI (region of interest). Then get lines by processing Hough transformation on each sub-region, after this phase, we can get a set of seed points. Finally, judge the lane´s type (straight or curve) by its slope. If it´s a curve line, fitting the lane by using the rational cubic B-spline curves. In the lane tracking part, we use Extended Kalman Filter to make sure continuous lane detection results. In the experiment part, we use a dataset which includes virtual videos and real videos to measure our method´s performance under the virtual and real environment. Experiment results show that the average detection rate of virtual video is 80%, and the real video is 85%. And the average processing time of virtual video is about 5.3ms/frame, and the real video is about 14.0ms/frame.