Fast edge-based stereo matching approach for road applications

Abstract

Several vision-based road applications use stereo vision algorithms, and they generally must be fast to be applied in real time. The main problem in stereo vision is the stereo matching problem, which consists in finding correspondences between two stereo images. In this paper, we present a new fast edge-based stereo matching approach devoted to road applications. Two passes of the dynamic programming algorithm are applied to estimate the final disparity map. The matching results of the first pass are only exploited to compute an initial disparity map (IDM). The so-called guiding edge points (GEPs) together with disparity ranges, i.e., possible matches, are derived from the IDM. In the second pass, the disparity ranges are used to reduce the search space as well as the mismatches and the GEPs to control and guide the matching process to the optimal solution. The proposed method has been tested on both real and virtual stereo images, it has been compared to a recently proposed method, and the results are satisfactory.