Scipediacontent: Scipediacontent moved page Draft Content 701576210 to Fritsch et al 2014a

2021-02-16T09:39:16Z

Scipediacontent moved page Draft Content 701576210 to Fritsch et al 2014a

Scipediacontent: Created page with " == 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 inform..."

2021-02-16T09:39:13Z

Created page with " == 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 inform..."

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== 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 paper, 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 capturing the local visual properties together with their spatial layout in the scene. In this paper, the extraction of road area and ego-lane on inner-city video streams is demonstrated. In particular, 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 graphics processing unit on a publicly available data set, demonstrating the performance on a variety of road types and weather conditions.

== Original document ==

The different versions of the original document can be found in:

* [http://xplorestaging.ieee.org/ielx7/6979/6869083/06766705.pdf?arnumber=6766705 http://xplorestaging.ieee.org/ielx7/6979/6869083/06766705.pdf?arnumber=6766705],
: [http://dx.doi.org/10.1109/tits.2014.2303899 http://dx.doi.org/10.1109/tits.2014.2303899]

* [https://dblp.uni-trier.de/db/journals/tits/tits15.html#FritschKK14 https://dblp.uni-trier.de/db/journals/tits/tits15.html#FritschKK14],
: [https://ieeexplore.ieee.org/document/6766705 https://ieeexplore.ieee.org/document/6766705],
: [http://ieeexplore.ieee.org/document/6766705 http://ieeexplore.ieee.org/document/6766705],
: [https://doi.org/10.1109/TITS.2014.2303899 https://doi.org/10.1109/TITS.2014.2303899],
: [https://dx.doi.org/10.1109/TITS.2014.2303899 https://dx.doi.org/10.1109/TITS.2014.2303899],
: [https://academic.microsoft.com/#/detail/2081303520 https://academic.microsoft.com/#/detail/2081303520]

* [https://pub.uni-bielefeld.de/record/2693383 https://pub.uni-bielefeld.de/record/2693383],
: [http://dx.doi.org/10.1109/TITS.2014.2303899 http://dx.doi.org/10.1109/TITS.2014.2303899] under the license https://rightsstatements.org/page/InC/1.0/

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Fritsch et al 2014a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 701576210 to Fritsch et al 2014a

Scipediacontent: Created page with " == 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 inform..."