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Optical Flow Estimation

Estimating the motion of every pixel in a sequence of images is a problem with many applications in computer vision, such as image segmentation, object classification,visual odometry, and driver assistance.

In general, optical flow describes a sparse or dense vector field, where a displacement vector is assigned to certain pixel position, that points to where that pixel can be found in another image. In the context of scene flow estimation, which is performed on images with additional depth values, every pixel is assigned a depth displacement as well.

Since much of the structural information of a 3D scene gets lost in the imaging process, so does the motion information. The estimation of the “correct” projected motion in an image sequence is therefore highly ill-posed and has to be aided by additional priors such as the regularity of the motion. Our group mainly focuses on optical flow estimation by means of variational methods, that allow a clear formulation of the assumptions incorporated into the estimation process and generally produce dense vector fields.

Contact: Caner Hazırbaş, Philip Häusser, Frank Steinbrücker

Related publications

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Book Chapters Journal Articles Conference and Workshop Papers 
Book Chapters
2006
A survey on variational optic flow methods for small displacements (J. Weickert, A. Bruhn, T. Brox, N. Papenberg), Chapter in Mathematical Models for Registration and Applications to Medical Imaging (O. Scherzer, ed.), Springer, volume 10, 2006. [bib]
Adaptive structure tensors and their applications (T. Brox, R. van den Boomgaard, F. B. Lauze, J. van de Weijer, J. Weickert, P. Mrázek, P. Kornprobst), Chapter in Visualization and Processing of Tensor Fields (J. Weickert, H. Hagen, eds.), Springer, 2006. [bib]
Book Chapters Journal Articles Conference and Workshop Papers 
Journal Articles
2014
Convex Relaxation of Vectorial Problems with Coupled Regularization (E. Strekalovskiy, A. Chambolle, D. Cremers), In SIAM Journal on Imaging Sciences, volume 7, 2014. [bib] [pdf]
2012
The Natural Total Variation Which Arises from Geometric Measure Theory (B. Goldluecke, E. Strekalovskiy, D. Cremers), In SIAM Journal on Imaging Sciences, volume 5, 2012. [bib] [pdf]
2011
Motion Field Estimation from Alternate Exposure Images (A. Sellent, M. Eisemann, B. Goldluecke, D. Cremers, M. Magnor), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 33, 2011. [bib] [pdf]
Stereoscopic Scene Flow Computation for 3D Motion Understanding (A. Wedel, T. Brox, T. Vaudrey, C. Rabe, U. Franke, D. Cremers), In International Journal of Computer Vision, volume 95, 2011. [bib] [pdf]
2006
Highly accurate optic flow computation with theoretically justified warping (N. Papenberg, A. Bruhn, T. Brox, S. Didas, J. Weickert), In International Journal of Computer Vision, volume 67, 2006. [bib]
2005
Motion Competition: A variational framework for piecewise parametric motion segmentation (D. Cremers, S. Soatto), In International Journal of Computer Vision, volume 62, 2005. [bib] [pdf]
2003
Statistical shape knowledge in variational motion segmentation (D. Cremers, C. Schnörr), In Image and Vision Computing, volume 21, 2003. [bib] [pdf]
Book Chapters Journal Articles Conference and Workshop Papers 
Conference and Workshop Papers
2016
A Convex Solution to Spatially-Regularized Correspondence Problems (T. Windheuser, D. Cremers), In European Conference on Computer Vision (ECCV), 2016. [bib] [pdf]
2015
FlowNet: Learning Optical Flow with Convolutional Networks (A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, T. Brox), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video],[code]) [bib] [pdf] [doi]
2011
Tight Convex Relaxations for Vector-Valued Labeling Problems (E. Strekalovskiy, B. Goldluecke, D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011. [bib] [pdf]
2010
Interactive Motion Segmentation (C. Nieuwenhuis, B. Berkels, M. Rumpf, D. Cremers), In Pattern Recognition (Proc. DAGM), Springer, volume 6376, 2010. [bib] [pdf]
Complex Motion Models for Simple Optical Flow Estimation (C. Nieuwenhuis, D. Kondermann, C. Garbe), In Pattern Recognition (Proc. DAGM), Springer, volume 6376, 2010. [bib] [pdf]
2009
Advanced Data Terms for Variational Optic Flow Estimation (F. Steinbruecker, T. Pock, D. Cremers), In Proceedings Vision, Modeling and Visualization (VMV), 2009. [bib] [pdf]
Reconstructing Optical Flow Fields by Motion Inpainting (B. Berkels, C. Nieuwenhuis, C. Garbe, M. Rumpf), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Springer, volume 5681, 2009. [bib] [pdf]
Large Displacement Optical Flow Computation without Warping (F. Steinbruecker, T. Pock, D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2009. [bib] [pdf]
Structure- and Motion-adaptive Regularization for High Accuracy Optic Flow (A. Wedel, D. Cremers, T. Pock, H. Bischof), In IEEE International Conference on Computer Vision (ICCV), 2009. [bib] [pdf]
Variational Optical Flow from Alternate Exposure Images (A. Sellent, M. Eisemann, B. Goldluecke, T. Pock, D. Cremers, M. Magnor), In Proceedings Vision, Modeling and Visualization (VMV), 2009. [bib] [pdf]
2008
An Improved Algorithm for TV-L1 Optical Flow (A. Wedel, T. Pock, C. Zach, D. Cremers, H. Bischof), In Proc. of the Dagstuhl Motion Workshop, Springer, 2008. [bib] [pdf]
Duality TV-L1 Flow with Fundamental Matrix Prior (A. Wedel, T. Pock, J. Braun, U. Franke, D. Cremers), In Image Vision and Computing, 2008. [bib] [pdf]
Efficient Dense Scene Flow from Sparse or Dense Stereo Data (A. Wedel, C. Rabe, T. Vaudrey, T. Brox, U. Franke, D. Cremers), In European Conference on Computer Vision (ECCV), 2008. [bib] [pdf]
2007
A Duality Based Algorithm for TV-L1-Optical-Flow Image Registration (T. Pock, M. Urschler, C. Zach, R. Beichel, H. Bischof), In 10th International Conference on Medical Image Computing and Computer Assisted Intervention, 2007. [bib]
A Duality Based Approach for Realtime TV-L1 Optical Flow (C. Zach, T. Pock, H. Bischof), In Pattern Recognition (Proc. DAGM), 2007. [bib]
2006
Variational motion segmentation with level sets (T. Brox, A. Bruhn, J. Weickert), In European Conference on Computer Vision (ECCV) (A. Leonardis, H. Bischof, A. Pinz, eds.), Springer, volume 3951, 2006. [bib] [pdf]
2004
High accuracy optical flow estimation based on a theory for warping (T. Brox, A. Bruhn, N. Papenberg, J. Weickert), In European Conference on Computer Vision (ECCV) (T. Pajdla, J. Matas, eds.), Springer, volume 3024, 2004. [bib] [pdf]Received "The Longuet-Higgins Best Paper Award"
2003
A generative model based approach to motion segmentation (D. Cremers, A. L. Yuille), In Pattern Recognition (Proc. DAGM) (B. Michaelis, G. Krell, eds.), Springer, volume 2781, 2003. [bib] [pdf]
Variational space-time motion segmentation (D. Cremers, S. Soatto), In IEEE International Conference on Computer Vision (ICCV) (B. Triggs, A. Zisserman, eds.), volume 2, 2003. [bib] [pdf]
2002
Statistical shape knowledge in variational motion segmentation (D. Cremers, C. Schnörr), In 1st Internat. Workshop on Generative-Model-Based Vision (A. Pece, Y. N. Wu, R. Larsen, eds.), Univ. of Copenhagen, 2002. (http://www.diku.dk/research/published/2002/02-01) [bib]
Nonlinear matrix diffusion for optic flow estimation (T. Brox, J. Weickert), In Pattern Recognition (Proc. DAGM) (L. van Gool, ed.), Springer, volume 2449, 2002. [bib] [pdf]
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Last edited 24.09.2015 21:16 by Caner Hazirbas