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Monocular vision based road detection methods are mostly based on machinelearning methods, relying on classification and feature extraction accuracy,and suffer from appearance, illumination and weather changes. Traditionalmethods introduce the predictions into conditional random fields or markovrandom fields models to improve the intermediate predictions based onstructure. These methods are optimization based and therefore resource heavyand slow, making it unsuitable for real time applications. We propose a methodto detect and segment roads with a random forest classifier of local expertswith superpixel based machine-learned features. The random forest takes inmachine learnt descriptors from a pre-trained convolutional neural network -VGG-16. The features are also pooled into their respective superpixels,allowing for local structure to be continuous. We compare our algorithm againstNueral Network based methods and Traditional approaches (based on Hand-craftedfeatures), on both Structured Road (CamVid and Kitti) and Unstructured RoadDatasets. Finally, we introduce a Road Scene Dataset with 1000 annotatedimages, and verify that our algorithm works well in non-urban and rural roadscenarios.

Unstructured Road Segmentation using Hypercolumn based Random Forests of Local experts