Weighted V-disparity approach for obstacles localization in highway environments

The employment of embedded passive sensors in order to perceive environment for reducing the accident risk level is a tendency of intelligent vehicles research. From such sensors, one can extract useful informations which can assist the driver to identify hazardous situations. While safety improvement is a substantial requirement for driving assistance, localizing and tracking obstacles in complex road environment became an important task. One promising approach is to use the V-disparity based on the stereovision technique. It is a cumulative space estimated from the disparity image. We propose a sound framework and a complete system based on a real-time stereovision for detection, 3D localization and tracking of dynamic obstacles in highway environment. The main contribution we propose is the improvement of the V-disparity approach by extending the basic approach by merging it with a confidence term. This consists on weighting each pixel in the V-disparity space according to a confidence value which measures the probability of associating a pair of pixels. Furthermore, we propose a tracking system which is based on the belief theory. The tracking task is done on the image space which takes into account uncertainties, handles conflicts, and automatically dealt with targets appearance and disappearce as well as their spatial and temporal propogation. Extensive experiments on simulated and real dataset demonstrate the effectiveness and the robustness of the weighted V-disparity approach.