A Comparative Analysis of Decision Trees Based Classifiers for Road Detection in Urban Environments

In this paper a comparative analysis of decision trees based classifiers is presented. Two different approaches are presented, the first one is a speficic classifier depending on the type of scene. The second one is a general classifier for every type of scene. Both approaches are trained with a set of features that enclose texture, color, shadows, vegetation and other 2D features. As well as 2D features, 3D features are taken into account, such as normals, curvatures and heights with respect to the ground plane. Several tests are made on five different classifiers to get the best parameters configuration and obtain the importance of each features in the final classification. In order to compare the results of this paper with the state of the art, the system has been tested on the KITTI Benchmark public dataset.