Road identification in monocular color images using random forest and color correlogram

This paper presents a system to identify road and non-road regions from monocular color images of paved and unpaved roads. Despite being a single object, the road in these images is subject to large changes in appearance due to environmental effects and track materials. This condition has challenged the practical application of road identification. The proposed system combines random forest with color correlogram to overcome such conditions and offers a classifier for road and non-road regions in traffic images. As a color feature, the color correlogram depicts the spatial correlation of color changes in an image. Using random forest, road identification is formulated as a learning paradigm. The combined effects of color correlograms and random forest create a robust system capable of identifying roads even in variable situations in real time. This combination is more effective than other combinations, such as a color histogram plus random forest, a color correlogram plus neural network, or a color histogram plus neural network.