Obstacles Detection on a Road by Dense Stereovision with 1D Correlation Windows and Fuzzy Filtering

In this paper, we propose an original approach to obstacles detection based on stereovision with mono-dimensional correlation windows. The result of the algorithm is a dense disparity map associated with a confidence map. For each pixel, correlation indices are computed for several widths of windows and several positions of the window centre. Three criteria, extracted from each correlation curve, are combined by a fuzzy filter to define a confidence measure. Our 1D method is compared to a classical 2D method and shows better results in term of errors and density rate. In the context of obstacle detection, we show that a basic segmentation of our disparity map yields a better detection and marking of the obstacles. The method is validated on synthetic image sequences and our results are compared with those obtained using a classical 2D method