This paper presents a fast stereo matching approach for road obstacle detection under foggy weather conditions. The stereo matching process can be treated as the problem of finding an optimal path on a 2D search plane. To obtain this path, we propose a new cost function. This last is derived from the variance values of the intensities on the right hand sides of the matched declivities. The matching process is executed independently for each scanline. In order to reduce the false matches and speed up the matching process, we propose to exploit the relationship between successive stereo images. So, the disparity map computed for one stereo pair will be used to find the disparity range for the next stereo pair. The disparity range is deduced for each scanline. The proposed approach has been tested on synthesized and real images under foggy weather conditions. The new method gives satisfactory results.
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