In Cremers, D., Rosenhahn, B., Yuille, A. L., & Schmidt, F. R., editors, *Statistical and Geometrical Approaches to Visual Motion Analysis*, of *Lecture Notes in Computer Science*, pages 23--45. Springer B. H., 2009. 00227

Paper abstract bibtex

Paper abstract bibtex

A look at the Middlebury optical flow benchmark [5] reveals that nowadays variational methods yield the most accurate optical flow fields between two image frames. In this work we propose an improvement variant of the original duality based TV-L 1 optical flow algorithm in [31] and provide implementation details. This formulation can preserve discontinuities in the flow field by employing total variation (TV) regularization. Furthermore, it offers robustness against outliers by applying the robust L 1 norm in the data fidelity term. Our contributions are as follows. First, we propose to perform a structure-texture decomposition of the input images to get rid of violations in the optical flow constraint due to illumination changes. Second, we propose to integrate a median filter into the numerical scheme to further increase the robustness to sampling artefacts in the image data. We experimentally show that very precise and robust estimation of optical flow can be achieved with a variational approach in real-time. The numerical scheme and the implementation are described in a detailed way, which enables reimplementation of this high-end method.

@incollection{ wedel_improved_2009, series = {Lecture {Notes} in {Computer} {Science}}, title = {An {Improved} {Algorithm} for {TV}-{L} 1 {Optical} {Flow}}, copyright = {©2009 Springer-Verlag Berlin Heidelberg}, isbn = {978-3-642-03060-4, 978-3-642-03061-1}, url = {http://link.springer.com/chapter/10.1007/978-3-642-03061-1_2}, abstract = {A look at the Middlebury optical flow benchmark [5] reveals that nowadays variational methods yield the most accurate optical flow fields between two image frames. In this work we propose an improvement variant of the original duality based TV-L 1 optical flow algorithm in [31] and provide implementation details. This formulation can preserve discontinuities in the flow field by employing total variation (TV) regularization. Furthermore, it offers robustness against outliers by applying the robust L 1 norm in the data fidelity term. Our contributions are as follows. First, we propose to perform a structure-texture decomposition of the input images to get rid of violations in the optical flow constraint due to illumination changes. Second, we propose to integrate a median filter into the numerical scheme to further increase the robustness to sampling artefacts in the image data. We experimentally show that very precise and robust estimation of optical flow can be achieved with a variational approach in real-time. The numerical scheme and the implementation are described in a detailed way, which enables reimplementation of this high-end method.}, language = {en}, number = {5604}, urldate = {2015-06-23TZ}, booktitle = {Statistical and {Geometrical} {Approaches} to {Visual} {Motion} {Analysis}}, publisher = {Springer B. H.}, author = {Wedel, Andreas and Pock, Thomas and Zach, Christopher and Bischof, Horst and Cremers, Daniel}, editor = {Cremers, Daniel and Rosenhahn, Bodo and Yuille, Alan L. and Schmidt, Frank R.}, year = {2009}, note = {00227}, pages = {23--45} }

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