The KITTI Vision Benchmark Suite

The method is based on multi-normalized histogram with Joint Boosting algorithm to road recognition. The approach could be summarized as a
three processes executed in parallel: (i) Image Segmentation, (ii) Texton Maps and (iii) Dispton Maps. In (i) is applied a combination of pre-filters with Watershed Transform to make the superpixel. In (ii) is executed a dense feature extraction based on 2D texture image and in (iii) is performed the disparity image to get 3D information. The combination of the results
generates an unified model of road class, based on distribution of Textons and Disptons applied in Joint Boosting algorithm.

\lambda = 15
h = 3

@INPROCEEDINGS{Gio_IV14,author={Vitor, G. B. and Victorino, A. C. and Ferreira, J. V.},
booktitle={ Workshop on Benchmarking Road Terrain and Lane Detection Algorithms for In-Vehicle Application on IEEE Intelligent Vehicles Symposium (IV)},
title={Comprehensive Performance Analysis of Road Detection Algorithms Using the Common Urban Kitti-Road Benchmark},
year={2014},
keywords={Road Recognition, Computer Vision, Artificial Neural Network, Joint Boosting, Texton Map, Dispton Map, Watershed Transform}
}