Road region segmentation based on sequential Monte-Carlo estimation

Road region is an important information for guidance of autonomous vehicles or robots. The goal of this research is to develop robust monocular algorithm suitable for region based road detection. This paper deals with a problem, how to estimate probability density function (pdf) of road region appearing in sequential images. The key idea is to construct pdf from temporal sequence of observations throughout the image sequence, where pdf has color components and spatial coordinates as its variables. The problem of pdf estimation was formulated in terms of Bayesian filtering, and sequential Monte-Carlo method was adopted as a tool to solve the problem. The proposed method was evaluated on real image sequences, and effectiveness of the proposed method is demonstrated.