Road Detection Based on Image Boundary Prior

As for vision based road detection, most of color based methods use a center-lower region as a “safe” road region to model road appearance. However, this region heavily relies on the pose of ego-vehicle. Color models trained by using samples from this region often yield biased results when some non-road regions are included. In this paper, we proposed a novel color based road detection method which can overcome this problem. It is based on an image boundary prior, which infers a road region by measuring the extent of the region connecting to the bottom boundary of an image. This prior is more robust than the center-lower prior. Moreover, we use illumination invariance color space for the distance metric of two neighboring regions in order to make our approach robust to shadows. Experiments demonstrate that the proposed method is superior to both the Gaussian mixture model based method and illumination invariance based method.