Alerting the Drivers about Road Signs with Poor Visual Saliency
Abstract
This paper proposes an improvement of Advanced Driver Assistance
System based on saliency estimation of road signs. After a road sign
detection stage, its saliency is estimated using a SVM learning. A
model of visual saliency linking the size of an object and a
size-independent saliency is proposed. An eye tracking experiment in
context close to driving proves that this computational evaluation
of the saliency fits well with human perception, and demonstrates
the applicability of the proposed estimator for improved ADAS.
Reference
@inproceedings{jpt-iv09b,
author = {Simon, L. and Tarel, J.-P. and Br\'emond, R.},
title = {Alerting the Drivers about Road Signs with Poor Visual Saliency},
booktitle = {Proceedings of IEEE Intelligent Vehicle Symposium (IV'2009)},
date = {June 3-5},
address = {Xian, China},
year = {2009},
pages = {48-53},
note = {http://perso.lcpc.fr/tarel.jean-philippe/publis/iv09b.html}
}
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