The KITTI Vision Benchmark Suite

This work proposes a method for road detection in
inner-city, using a set of probabilistic distribution to model the classifier of a Joint Boosting algorithm. Differently from others works, this approach creates a different set of features merging a technique called Diston, proposed by previous works (3D information) with the Texton (2D texture and color) to compute a set of probabilistic distribution for each superpixel. The probabilistic distribution feature’s descriptor is used to model the weak classifier used in the Joint Boosting algorithm.

lambda = 15
h = 3

@INPROCEEDINGS{Vitor2014ICRAWORK,
author={Vitor, G. B. and Victorino, A. C. and Ferreira, J. V.},
booktitle={ Workshop on Modelling, Estimation, Perception and Control of All Terrain Mobile Robots on IEEE International Conference on Robotics and Automation (ICRA)},
title={A probabilistic distribution approach for the classification of urban roads in complex environments},
year={2014},
keywords={Road Detection, Computer Vision, Joint Boosting, Texton Map, Dispton Map, Watershed Transform.},

}