Road detection from a monocular camera is a perception module in any advanced driver assistance or autonomous driving system. Traditional techniques work reasonably well for this problem when the roads are well maintained and the boundaries are clearly marked. However, in many developing countries or even for the rural areas in the developed countries, the assumption does not hold which leads to failure of such techniques. In this paper, we propose a novel technique based on the combination of deep convolutional neural networks (CNNs), along with color lines model based prior in a conditional random field (CRF) framework. While the CNN learns the road texture, the color lines model allows adapting to varying illumination conditions. We show that our technique outperforms the state of the art segmentation techniques on the unmarked road segmentation problem. Though, not a focus of this paper, we show that even on the standard benchmark datasets like KITTI and CamVid, where the road boundaries are well marked, the proposed technique performs competitively to the contemporary techniques.