Towards video processing in vehicles under adverse weather conditions

In this contribution we propose methods for vehicle detection and tracking for the Advanced Driver Assistance Systems (ADAS) that work under extremely adverse weather conditions. Most of the state-of-the-art vehicle detection and tracking methods are based either on appearance based vehicle recognition or on extraction and tracking of dedicated image key points. Visibility deterioration due to rain drops and water streaks on the windshield, swirling spray, and fog lead to a drastic performance reduction or even to a complete failure of these approaches. In this contribution we propose several methods for coping with these phenomena. In addition to an extension of the feature-based tracking method, which copes with outliers and temporarily disappearing key points, we present a detection and tracking method based on search for vehicle rear lights and whole rear views in the saturation channel. Utilization of symmetry operators and search space restriction allows to detect and track vehicles even in pouring rain conditions. Furthermore, we present two applications of the above-described methods. Estimation of the strength of spray produced by preceding vehicles allows to draw conclusions about the overall visibility conditions and to adjust the intensity of one's own rear lights. Besides, a restoration of deteriorated image regions becomes possible.