Robust Lane Marking Detection by the Half Quadratic Approach
Abstract
Automatic road marking detection is a key point in road scene
analysis, whose applications concern as well the inventory of lane
marking on the road network as the design of assistance systems
on-board vehicles. To this end, we propose in this document a model
which accounts for the geometric variability of the markings and,
above all, which is robust to the numerous perturbations that can be
observed in real-world images. The detection problem is formalized
as an estimation problem. This allows to associate each result with
a confidence measure. Such a self-evaluation capacity is indeed
necessary when integrating road marking detection into more complex
systems, such as lane following applications. We present here
algorithms that can be derived thanks to the half-quadratic approach
of statistical robust estimation, which we revisit in a Lagrangian
formalism. The proposed approach allows a straightforward extension
of the estimation algorithms to the simultaneous fitting of multiple
marking lines. These results were obtained in collaboration with
DESE (LCPC), LIVIC (INRETS/LCPC), ERA 27 (LRPC Strasbourg), and ERA
17 (LRPC Angers), between 2002 and 2007. They lead to an
operational, real-time guidance system, which was successfully
tested within the context of the ARCOS 2004 project. Moreover, a
multiple lines detection algorithm is routinely used for calibrating
the MLPC IRCAN (French acronym for Road Imagery using Numeric
CAmeras) systems.
Reference
@BOOK{jpt-rr07,
author = {Tarel, J.-P. and Ieng, S.-S. and Charbonnier, P.},
title = {Robust Lane Marking Detection by the Half Quadratic Approach},
publisher = {LCPC},
series = {Collection Etudes et Recherches des Laboratoires des Ponts et Chauss\'ees, CR 49},
month = nov,
year = {2007},
pages = {74},
url = {http://perso.lcpc.fr/tarel.jean-philippe/publis/rr07.html}
}
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