Lanes Detection Based on Unsupervised and Adaptive Classifier

This paper describes an algorithm to detect the road lanes based on an unsupervised and adaptive classifier. We have selected this classifier because in the road we do not know the parameters of lanes, although we know that lanes are there, only they need to be classified. First of all, we tested and measured the brightness of the lanes of the road in many videos. Generally, the lines on the road are white. We used the HSV image and we improved the region of study. Then, we used a Hough transform which yields a set of possible lines. These lines have to be classified. The classifier starts with initial parameters because we suppose that the vehicle is on road and in the center of the lane. There are two classes, the first one is the left road line and the second one is the right road line. Each line has two parameters that are: middle point of line and the line slope. These parameters will be changing in order to adjust to the real lanes. A tensor holds the two lines, so these lines will not separate more than the tensor allows. A Kalman filter estimates the new class´s parameters and improves the tracking of the lanes. Finally, we use a mask in order to highlight the lane and show to the user a better image.