Drivable road region detection using homography estimation and efficient belief propagation with coordinate descent optimization

Road detection is one of the key issues for the implementation of intelligent vehicles. In this paper, we present a drivable road region detection method using homography estimation and efficient belief propagation. In the method, each pixel in stereo images is assigned a label by minimizing an energy function that accounts for the planar road region, which is defined by utilizing the 2D projective transformations of stereo information and the inference algorithm in binary piecewise Markov Random Field (MRF). The energy is minimized in coordinate descent iterations that alternate between optimizing the homography induced by the planar road plane and implementing efficient belief propagation to find the optimal binary labeling that segments the image into two non-overlapping road and non-road regions. In the optimization process, both image evidence and temporal information are used; meanwhile, an error correction mechanism is applied. Therefore, more accurate as well as robust detection of the road region can be expected. Experimental results on a wide variety of typical but challenging real road scenes have substantiated the effectiveness as well as robustness of the proposed method.