A classification of on-road obstacles according to their relative velocities

Abstract

The systems based on image processing have numerous applications in the domain of motion control of robots and autonomous vehicles. The current paper is oriented to the solution of the problem that precedes the implementation of automatic avoidance of the on-road obstacles-how to detect them, to track in the sequence of images, and to recognize which of them are stationary, incoming, or outgoing from the camera. The overall algorithm of obstacle classification presented in this paper consists of three basic phases: (1) image segmentation in order to extract the pixels belonging to the image of a road and the objects over it; (2) extraction of characteristic points inside the area of the obstacle, their description and tracking in following frames; and (3) estimation of distances between the camera, the obstacles and their rates of change (relative velocities). The verifications of particular steps of the proposed algorithm are illustrated using real road-traffic images, while the overall algorithm is tested using both synthesized sequences of images and the ones acquired in real driving.

Unpaved road detection based on spatial fuzzy clustering algorithm

“…There is no doubt that road detection has become one of the most popular topics in computer vision [1][2][3]. Computer vision technology is very suitable for road detection since it includes a large amount of detection information and accurate sensing [4,5].…”