A Hierarchical Segmentation Approach towards Roads and Slopes for Collapse Recognition

Abstract

Color image processing is widely used in Intelligent Transport System, but seldom used in recognition of roads and slopes collapse. The application can reduce time and efforts. And the roads and slopes segmentation is the first and key step of the recognition system, which is a challenging and difficult problem. One of the problems is the presence of different types of roads and slopes. In this paper, we propose a novel framework for segmenting road images in a hierarchical manner that can separate the following objects: road and slopes with or without collapse, sky, road signs, cars, buildings and vegetation from the images. Then the Region of Interests (ROIs), i.e. the roads and slopes, are obtained with the geometrical, location of the objects and statistical color features which are extracted based on L*a*b color space and Gabor filter. According to combination K-means clustering with region merging, connected-component algorithm and morphological operation, the roads and slopes are segmented. The hierarchical approach does not assume the roads are present in the same type and assume the road images can be captured from arbitrary angles. The experiments show that the approach in this paper can achieve a satisfied result on various road images.