Road detection based on superpixels and anisotropic heat diffusion

Road detection is a basic functionality for driver assistance system such as vehicle and pedestrian detection. The main challenge is to deal with the presence of different objects (pedestrians and vehicles), the continuously varying background, different imaging conditions (different viewpoints, varying weather conditions and changing illumination), different road types (color, shape) and different environments (off-road, urban, highways). Accordingly, we propose a novel road detection approach based on superpixels and anisotropic heat diffusion. The idea is to previously denoise and deblur road images using directional filters. And then to oversegment the enhanced image into small homogeneous regions which are called superpixels with the superpixel segmentation method. According to the thermodynamic energy diffusion theory, we cluster superpixels into several region blocks. Finally, the algorithm extracts the road region combining the prior knowledge with the largest and most coherent principle of cosegmentation. Quantitative and qualitative experiments show that the approach is robust to lighting variations, heavy traffic and shadows. Moreover, the proposed method provides the better performance when compared with several state-of-the-art methods.