Unstructured Road Segmentation using Hypercolumn based Random Forests of Local experts

Road detection is an important task for autonomous land based vehicles and robots alike. For both, robots and vehicles, high level decisions in the pipeline are after the road detection is performed. Monocular vision based road detection methods are mostly based on machine learning methods, relying on classification and feature extraction accuracy. However, this is highly affected by appearance, illumination and weather changes. Traditional methods introduce the predictions into conditional random fields or markov random fields models to improve the intermediate predictions based on structure. These methods are optimization based and therefore resource heavy and slow, making it unsuitable for real time applications. In this paper, we present a method to detect and segment roads with a random forest classifier of local experts with superpixel based machine-learned features. The random forest takes in machine 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 against Nueral Network based methods and Traditional approaches (based on Hand-crafted features), on both Structured Road (CamVid and Kitti) and Unstructured Road Datasets. Finally, we introduce a Road Scene Dataset with 1000 annotated images, and verify that our algorithm works well in non-urban and rural road scenarios.