Efficient and robust classification method using combined feature vector for lane detection

The aim of this paper is to develop a method for low-cost and accurate classification of highways and rural ways image pixels for lane detection. The method uses three main components: adaptive/predefined image splitting, subimage level classification and class merging based on homogeneity checking conditions. In the first step, a preclassification in road and nonroad pixels is carried out, on the resized input image, using the decision tree method. As a result of this first step we obtain the road reference feature value, and the lane-markings positions in case of highways. For the rural ways image splitting we use a predefined division method, and for the highways we use an adaptive division method based on the detected lane-markings. The proposed classification is carried out on the subimages using the K-mean classifier on a composed gray and texture based feature vector. The gray feature vector is fixed in the preclassification phase, and the texture feature vector is only updated during the classification is performed. This way the convergence is much faster and the classification accuracy is better. The resulting road and nonroad classes of subimages are merged into a road and a nonroad class using a homogeneity criterion based on the road reference feature value. Next, a forward and backward method is used to detect borders of the road region. Finally, we use the Kalman filter and the Bresenhem line drawing to connect the border pixels.