Real-time Obstacle Detection by Road Plane Segmentation

Use of computer vision in the automobile industry has been showing extensive growth for past several years. Such camera based systems assist the driver in making decisions with regard to road safety and host vehicle safety. This paper presents a semantic based road plane segmentation technique to detect moving and non-moving obstacles and warn the driver about perils around the automobile. The technique uses color information of the pixels to create primary road models. Having these models as a basis, dynamic models, for including various road related anomalies, are also computed. Next, both the models are merged and finally used to classify incoming image pixels as the road or the obstacles. Application of the technique on several videos, obtained from a moving camera, mounted on a car, resulted in a significantly high detection of moving and non-moving objects in the Region of Interest (RoI). Computational complexity of the technique is very less and it has been ported on a digital signal processor. The technique can be employed in back, side and front cameras to detect the obstacles for the road and host vehicle safety.