The KITTI Vision Benchmark Suite

This method presents an approach for road detection based on image segmentation. This segmentation is resulted from merging 2D and 3D image processing data from a stereo vision system. The 2D layer returns a matrix containing
pixel’s clusters based on the Watershed transform. Whereas the 3D layer return labels, that are classified by the V-Disparity technique, to free spaces, obstacles and non-classified area. Thus, a feature’s descriptor for each cluster is composed with features from both layers. The road pattern recognition was performed by an artificial neural network, trained to obtain a final result from this feature’s descriptor.

(Some alterations from original code were done to adapt to this benchmark.)

\lambda=30
h = 3

@INPROCEEDINGS{Vitor2013IV,
author={Vitor, G. B. and Lima, D. A. and Victorino, A. C. and Ferreira, J. V.},
booktitle={Intelligent Vehicles Symposium (IV), 2013 IEEE},
title={A 2D/3D Vision Based Approach Applied to Road Detection in Urban Environments},
year={2013},
keywords={Road Detection; Computer Vision; Image Segmentation; Watershed Transform; V-Disparity Map.},
pages={952-957}
}