Dirt road segmentation using color and texture features in color imagery

This paper proposes a method for segmenting an unstructured dirt road in color space images using color and texture analysis. A support vector machine (SVM) classifier was trained on samples of on and off road patches from a similar road. Image patches were classified at sparse intervals at a fixed distance from the vehicle. Each patch is described by the Histogram of oriented gradients (HOG), the Local Binary Patters (LBP), a histogram of the color channel, and a histogram of a non linear color transform. The classified patches were transformed to the next frame of the sequence using the scale invariant feature transform (SIFT) to reduce reclassification of image patches. Morphological opening and closing were used to transform the points into a mask, and reduce errors. Experimental results indicated that the algorithm can accurately segment road images given a set of training data from similar road utilizing only color imagery.