Towards a robust and fast color stereo matching for intelligent vehicle application

A fast and efficient color stereo matching algorithm is presented in this paper. The intended application is in the field of intelligent vehicles. The algorithm is initialized with selecting an appropriate color space giving the smallest disparity error. Dynamic cross-based aggregation region is then applied. To be fast and also robust to noise and illumination variation, the algorithm explores sparse and strategic census mask. The algorithm is accelerated with GPU implementation. The performances of the proposed algorithm are tested on Middlebury¹ dataset images as well as on simulated road traffic scenes of TNO MARS/Prescan² database. Experiments show that our results perform better than the top performer method in the literature. Some limitations of the method are discussed at the end of the paper.