The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
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
Dense point trajectories by GPU-accelerated large displacement optical flow
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
Fast cost-volume filtering for visual correspondence and beyond
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A new three-step search algorithm for block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Modeling temporal coherence for optical flow
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
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Despite the significant progress in terms of accuracy achieved by recent variational optical flow methods, the correct handling of large displacements still poses a severe problem for many algorithms. In particular if the motion exceeds the size of an object, standard coarse-to-fine estimation schemes fail to produce meaningful results. While the integration of point correspondences may help to overcome this limitation, such strategies often deteriorate the performance for small displacements due to false or ambiguous matches. In this paper we address the aforementioned problem by proposing an adaptive integration strategy for feature matches. The key idea of our approach is to use the matching energy of the baseline method to carefully select those locations where feature matches may potentially improve the estimation. This adaptive selection does not only reduce the runtime compared to an exhaustive search, it also improves the reliability of the estimation by identifying unnecessary and unreliable features and thus by excluding spurious matches. Results for the Middlebury benchmark and several other image sequences demonstrate that our approach succeeds in handling large displacements in such a way that the performance for small displacements is not compromised. Moreover, experiments even indicate that image sequences with small displacements can benefit from carefully selected point correspondences.