IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual reconstruction
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Performance of optical flow techniques
International Journal of Computer Vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Robot Vision
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Towards Ultimate Motion Estimation: Combining Highest Accuracy with Real-Time Performance
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Consistent Segmentation for Optical Flow Estimation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Over-Parameterized Variational Optical Flow
International Journal of Computer Vision
Particle Video: Long-Range Motion Estimation Using Point Trajectories
International Journal of Computer Vision
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
A Segmentation Based Variational Model for Accurate Optical Flow Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Residual Images Remove Illumination Artifacts!
Proceedings of the 31st DAGM Symposium on Pattern Recognition
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Fusion Moves for Markov Random Field Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
International Journal of Computer Vision
Bilateral filtering-based optical flow estimation with occlusion detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Learning to find occlusion regions
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A polar representation of motion and implications for optical flow
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing moving images with layers
IEEE Transactions on Image Processing
Layered segmentation and optical flow estimation over time
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Low level vision via switchable Markov random fields
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Decomposing and regularizing sparse/non-sparse components for motion field estimation
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Are we ready for autonomous driving? The KITTI vision benchmark suite
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Optical flow estimation using learned sparse model
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Motion Detail Preserving Optical Flow Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enabling warping on stereoscopic images
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Efficient nonlocal regularization for optical flow
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
A naturalistic open source movie for optical flow evaluation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
A Fully-Connected Layered Model of Foreground and Background Flow
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
Large Displacement Optical Flow from Nearest Neighbor Fields
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
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The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that "classical" flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on the Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.