Sequential Monte-Carlo Based Road Region Segmentation Algorithm with Uniform Spatial Sampling

Vision based road recognition and tracking are crucial tasks in a field of autonomous driving. Road recognition methods based on shape analysis of road region have the potential to overcome the limitations of traditional boundary based approaches, but a robust method for road region segmentation is the challenging issue. In our work, we treat the problem of road region segmentation as a classification task, where road pixels are classified by statistical decision rule based on the probability density function (pdf) of road features. This paper presents a new algorithm for the estimation of the pdf, based on sequential Monte-Carlo (SMC) method. The proposed algorithm is evaluated on data sets of three different types of images, and the results of evaluation show the effectiveness of the proposed method.