Fast and Spatially-Smooth Terrain Classification Using Monocular Camera

Abstract

In this paper, we present a monocular camera based terrain classification scheme. The uniqueness of the proposed scheme is that it inherently incorporates spatial smoothness while segmenting a image, without requirement of post-processing smoothing methods. The algorithm is extremely fast because it is build on top of a Random Forest classifier. We present comparison across features and classifiers. The baseline algorithm uses color, texture and their combination with classifiers such as SVM and Random Forests. We further enhance the algorithm through a label transfer method. The efficacy of the proposed solution can be seen as we reach a low error rates on both our dataset and other publicly available datasets.