TY  - CONF
AU  - Jose Manuel Alvarez
AU  - Felipe Lumbreras
AU  - Theo Gevers
AU  - Antonio Lopez
A2  - IV
PY  - 2010//
TI  - Geographic Information for vision-based Road Detection
BT  - IEEE Intelligent Vehicles Symposium
SP  - 621–626
KW  - road detection
N2  - Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach.
UR  - https://ieeexplore.ieee.org/document/5548002
L1  - http://158.109.8.37/files/ALG2010.pdf
UR  - http://dx.doi.org/10.1109/IVS.2010.5548002
N1  - ADAS;ISE
ID  - Jose Manuel Alvarez2010
ER  -