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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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




    Publication date :

    2015-09-01


    Size :

    2238518 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Vision-based robust road lane detection in urban environments

    Beyeler, Michael / Mirus, Florian / Verl, Alexander | IEEE | 2014


    A Road Safety Analysis in Urban Environments

    Mussone, L. / Reitani, G. | British Library Conference Proceedings | 1998


    A road safety analysis in urban environments

    Mussone,L. / Reitani,G. / Polytechnic of Milan,Dep.of Transport Systems and Mobility,IT et al. | Automotive engineering | 1998