Illumination invariant road detection based on learning method

Bongjoe Kim, Jongin Son, Kwanghoon Sohn

Abstract: Road detection is an essential and important component in intelligent transportation system (ITS). Generally, most road detection methods are sensitive to variation of illumination which results in increasing false detection rate. In this paper, we propose an illumination invariant road detection method to deal with variation of illumination. We adopt learning method to estimate illumination invariant direction which is specified to road surface. Once this direction is estimated, we can classify image pixel as road or not. Incorporating scene layout of road image, we reduce false positive detection rate outside the road. Experimental results on real road scenes show that the effectiveness of the proposed method.