Robust Road Region Extraction in Video Under Various Illumination and Weather Conditions

Abstract

Robust road region extraction plays a crucial role in many computer vision applications, such as automated driving and traffic video analytics. Various weather and illumination conditions like snow, fog, dawn, daytime, and nighttime often pose serious challenges to automated road region detection. This paper presents a new real-time road recognition method that is able to accurately extract the road region in traffic videos under adverse weather and illumination conditions. Specifically, the novel global foreground modeling (GFM) method is first applied to subtract the ever-changing background in the traffic video frames and robustly detect the moving vehicles which are assumed to drive in the road region. The initial road samples are then obtained from the subtracted background model in the location of the moving vehicles. The integrated features extracted from both the grayscale and the RGB and HSV color spaces are further applied to construct a probability map based on the standardized Euclidean distance between the feature vectors. Finally, the robust road mask is derived by integrating the initially estimated road region and the regions located by the flood-fill algorithm. Experimental results using a dataset of real traffic videos demonstrate the feasibility of the proposed method for automated road recognition in real-time.