@article {PUBA275,
	title = {Monocular Road Terrain Detection by Combining Visual and Spatial Information},
	author = {Jannik Fritsch AND Tobias K{\"u}hnl AND Franz Kummert},
	year = {2014},
	abstract = {For future driver assistance systems and autonomous vehicles, the road course, i.e., the width and shape of the driving path, is an important source of information. In this contribution we introduce a new hierarchical two-stage approach for learning the spatial layout of road scenes. In the first stage, base classifiers analyze the local visual properties of patches extracted from monocular camera images and provide metric confidence maps. We use classifiers for road appearance, boundary appearance, and lane-marking appearance. The core of the proposed approach is the computation of SPatial RAY (SPRAY) features from each metric confidence map in the second stage. A boosting classifier selecting discriminative SPRAY features can be trained for different types of road terrain and allows to capture the local visual properties together with their spatial layout in the scene. In this contribution, the extraction of road area and ego-lane on inner city video streams is demonstrated. Especially the detection of the ego-lane is a challenging semantic segmentation task showing the power of SPRAY features, because on a local appearance level, the ego-lane is not distinguishable from other lanes. We have evaluated our approach operating at 20 Hz on a GPU on a publicly available dataset, demonstrating the performance on a variety of road types and weather conditions.
},
	publisher = {IEEE},
	journal = {IEEE Transactions on Intelligent Transportation Systems}
}