Shadow resistant road segmentation from a mobile monocular system

An essential functionality for advanced driver assistance systems (ADAS) is road segmentation, which directly supports ADAS applications like road departure warning and is an invaluable background segmentation stage for other functionalities as vehicle detection. Unfortunately, road segmentation is far from being trivial since the road is in an outdoor scenario imaged from a mobile platform. For instance, shadows are a relevant problem for segmentation. The usual approaches are ad hoe mechanisms, applied after an initial segmentation step, that try to recover road patches not included as segmented road for being in shadow. In this paper we argue that by using a different feature space to perform the segmentation we can minimize the problem of shadows from the very beginning. Rather than the usual segmentation in a color space we propose segmentation in a shadowless image which is computable in real-time using a color camera. The paper presents comparative results for both asphalted and non-asphalted roads, showing the benefits of the proposal in presence of shadows and vehicles.