Detecting lane markers in complex urban environments

In this paper, we present a new methodology for detecting lane markers that is able to withstand many challenging situations like scattered shadows, illumination changes, and presence of neighboring vehicles to name a few. At first, the input image undergoes a perspective removal followed by a color space conversion. Then, the core elements consisting of template matching, lane region merging, and elliptical projections are explained. Finally, the developed system showed good results when tested on 15 minutes of real-world driving videos containing variations in illumination, traffic, and road surface conditions.