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MULTI-CLASSIFICATION FOR ROAD DETECTION IN UNSUPERVISED ENVIRONMENT

The aim of this paper is to develop a method for low-cost and accurate classification of high way and rural way image pixels for lane detection. The method uses three main elements: adaptive image splitting, sub-image level classification and homogeneity based merging. The classification is carried out on the resulting sub-images using a K-mean. After classification through a merging process the classified road and unroad classes are grouped into road and unroad regions, respectively. A forward and backward method is used to detect borders of the road region after grouping. Finally, we use the Kalman filter and the Bresenhem line drawing to connect the border pixels.