Support vector machines based stereo matching method for advanced driver assistance systems

Abstract

Stereo vision is a measurement method for finding correspondence between two or more input images in order to obtain a detailed 3D representation of a scene. This paper presents an approach for matching stereo sequences acquired by a stereo sensor mounted on an intelligent vehicle. The approach uses machine learning alongside spatio-temporal information to predict the matching results. This means that the obtained matching results during the previous frame are used as a training samples for a support vector machine classifier, as well as to derive disparity ranges for each scan-line, which are then used to predict the matching of the current frame. The distance to the hyper-plan computed by the SVM is used as a cost function to fill a 2D search space. Then, the dynamic programming algorithm is performed for matching edge points in the stereo pair. Experiments on both virtual and real stereo image sequences have been conducted, demonstrating satisfactory performance.