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The increasing importance of outdoor applications such as driver assistance systems or video surveillance tasks has recently triggered the development of optical flow methods that aim at performing robustly under uncontrolled illumination. Most of these methods are based on patch-based features such as the normalized cross correlation, the census transform or the rank transform. They achieve their robustness by locally discarding both absolute brightness and contrast. In this paper, we follow an alternative strategy: Instead of discarding potentially important image information, we propose a novel variational model that jointly estimates both illumination changes and optical flow. The key idea is to parametrize the illumination changes in terms of basis functions that are learned from training data. While such basis functions allow for a meaningful representation of illumination effects, they also help to distinguish real illumination changes from motion-induced brightness variations if supplemented by additional smoothness constraints. Experiments on the KITTI benchmark show the clear benefits of our approach. They do not only demonstrate that it is possible to obtain meaningful basis functions, they also show state-of-the-art results for robust optical flow estimation. | The increasing importance of outdoor applications such as driver assistance systems or video surveillance tasks has recently triggered the development of optical flow methods that aim at performing robustly under uncontrolled illumination. Most of these methods are based on patch-based features such as the normalized cross correlation, the census transform or the rank transform. They achieve their robustness by locally discarding both absolute brightness and contrast. In this paper, we follow an alternative strategy: Instead of discarding potentially important image information, we propose a novel variational model that jointly estimates both illumination changes and optical flow. The key idea is to parametrize the illumination changes in terms of basis functions that are learned from training data. While such basis functions allow for a meaningful representation of illumination effects, they also help to distinguish real illumination changes from motion-induced brightness variations if supplemented by additional smoothness constraints. Experiments on the KITTI benchmark show the clear benefits of our approach. They do not only demonstrate that it is possible to obtain meaningful basis functions, they also show state-of-the-art results for robust optical flow estimation. | ||
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* [http://www.mia.uni-saarland.de/Publications/demetz-eccv14.pdf http://www.mia.uni-saarland.de/Publications/demetz-eccv14.pdf] | * [http://www.mia.uni-saarland.de/Publications/demetz-eccv14.pdf http://www.mia.uni-saarland.de/Publications/demetz-eccv14.pdf] | ||
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| + | * [http://link.springer.com/content/pdf/10.1007/978-3-319-10590-1_30 http://link.springer.com/content/pdf/10.1007/978-3-319-10590-1_30], | ||
| + | : [http://dx.doi.org/10.1007/978-3-319-10590-1_30 http://dx.doi.org/10.1007/978-3-319-10590-1_30] under the license http://www.springer.com/tdm | ||
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| + | * [https://dblp.uni-trier.de/db/conf/eccv/eccv2014-1.html#DemetzSVWB14 https://dblp.uni-trier.de/db/conf/eccv/eccv2014-1.html#DemetzSVWB14], | ||
| + | : [http://www.mia.uni-saarland.de/Publications/demetz-eccv14.pdf http://www.mia.uni-saarland.de/Publications/demetz-eccv14.pdf], | ||
| + | : [https://link.springer.com/chapter/10.1007/978-3-319-10590-1_30 https://link.springer.com/chapter/10.1007/978-3-319-10590-1_30], | ||
| + | : [https://www.scipedia.com/public/Demetz_et_al_2014a https://www.scipedia.com/public/Demetz_et_al_2014a], | ||
| + | : [https://rd.springer.com/chapter/10.1007/978-3-319-10590-1_30 https://rd.springer.com/chapter/10.1007/978-3-319-10590-1_30], | ||
| + | : [https://doi.org/10.1007/978-3-319-10590-1_30 https://doi.org/10.1007/978-3-319-10590-1_30], | ||
| + | : [https://academic.microsoft.com/#/detail/195690893 https://academic.microsoft.com/#/detail/195690893] | ||
Latest revision as of 16:31, 21 January 2021
Abstract
The increasing importance of outdoor applications such as driver assistance systems or video surveillance tasks has recently triggered the development of optical flow methods that aim at performing robustly under uncontrolled illumination. Most of these methods are based on patch-based features such as the normalized cross correlation, the census transform or the rank transform. They achieve their robustness by locally discarding both absolute brightness and contrast. In this paper, we follow an alternative strategy: Instead of discarding potentially important image information, we propose a novel variational model that jointly estimates both illumination changes and optical flow. The key idea is to parametrize the illumination changes in terms of basis functions that are learned from training data. While such basis functions allow for a meaningful representation of illumination effects, they also help to distinguish real illumination changes from motion-induced brightness variations if supplemented by additional smoothness constraints. Experiments on the KITTI benchmark show the clear benefits of our approach. They do not only demonstrate that it is possible to obtain meaningful basis functions, they also show state-of-the-art results for robust optical flow estimation.
Original document
The different versions of the original document can be found in:
- http://dx.doi.org/10.1007/978-3-319-10590-1_30 under the license http://www.springer.com/tdm
- http://www.mia.uni-saarland.de/Publications/demetz-eccv14.pdf,
- https://link.springer.com/chapter/10.1007/978-3-319-10590-1_30,
- https://www.scipedia.com/public/Demetz_et_al_2014a,
- https://rd.springer.com/chapter/10.1007/978-3-319-10590-1_30,
- https://doi.org/10.1007/978-3-319-10590-1_30,
- https://academic.microsoft.com/#/detail/195690893
Document information
Published on 01/01/2014
Volume 2014, 2014
DOI: 10.1007/978-3-319-10590-1_30
Licence: CC BY-NC-SA license
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