Road Obstacle Detection Using Robust Model Fitting

Abstract

Awareness of pedestrians, other vehicles, and other road obstacles is key to driving safety, and so their detection is a critical need in driver assistance research. We propose using a model-based approach which can either directly segment the disparity to detect obstacles or remove the road regions from an already segmented disparity map. We developed two methods for segmentation: first, by directly segmenting obstacles from the disparity map; and, second by using morphological operations followed by a robust model fitting algorithm to reject road segments after the segmentation process. To test the success of our methods, we have tested and compared them with an available method in the literature.