Simultaneous Contrast Restoration and Obstacles Detection: First Results

Reliable obstacles detection under adverse weather conditions, especially foggy conditions, is a challenging task because the contrast is drastically reduced. Consequently, the classical approaches relying on pattern recognition techniques or points of interest matching are not so efficient anymore. In this paper, a novel approach is proposed which is able to simultaneously restore the contrast of the scene and to detect the presence of obstacles by stereovision once the atmosphere opacity is known. The different computation stages are detailed: fog density estimation, contrast enhancement, local distortions detection and obstacles detection, as well as their combination. The method is illustrated and partially assessed thanks to a video sequence under foggy weather. Finally, future research directions are indicated.