DeepFlow: Large Displacement Optical Flow with Deep Matching
Optical flow computation is a key component in many computer vision systems designed for tasks such as action detection or activity recognition. However, despite several major advances over the last decade, handling large displacement in optical flow remains an open problem. Inspired by the large di...
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Published in | 2013 IEEE International Conference on Computer Vision pp. 1385 - 1392 |
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Main Authors | , , , |
Format | Conference Proceeding Journal Article |
Language | English |
Published |
IEEE
01.12.2013
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Subjects | |
Online Access | Get full text |
ISSN | 1550-5499 |
DOI | 10.1109/ICCV.2013.175 |
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Abstract | Optical flow computation is a key component in many computer vision systems designed for tasks such as action detection or activity recognition. However, despite several major advances over the last decade, handling large displacement in optical flow remains an open problem. Inspired by the large displacement optical flow of Brox and Malik, our approach, termed Deep Flow, blends a matching algorithm with a variational approach for optical flow. We propose a descriptor matching algorithm, tailored to the optical flow problem, that allows to boost performance on fast motions. The matching algorithm builds upon a multi-stage architecture with 6 layers, interleaving convolutions and max-pooling, a construction akin to deep convolutional nets. Using dense sampling, it allows to efficiently retrieve quasi-dense correspondences, and enjoys a built-in smoothing effect on descriptors matches, a valuable asset for integration into an energy minimization framework for optical flow estimation. Deep Flow efficiently handles large displacements occurring in realistic videos, and shows competitive performance on optical flow benchmarks. Furthermore, it sets a new state-of-the-art on the MPI-Sintel dataset. |
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AbstractList | Optical flow computation is a key component in many computer vision systems designed for tasks such as action detection or activity recognition. However, despite several major advances over the last decade, handling large displacement in optical flow remains an open problem. Inspired by the large displacement optical flow of Brox and Malik, our approach, termed Deep Flow, blends a matching algorithm with a variational approach for optical flow. We propose a descriptor matching algorithm, tailored to the optical flow problem, that allows to boost performance on fast motions. The matching algorithm builds upon a multi-stage architecture with 6 layers, interleaving convolutions and max-pooling, a construction akin to deep convolutional nets. Using dense sampling, it allows to efficiently retrieve quasi-dense correspondences, and enjoys a built-in smoothing effect on descriptors matches, a valuable asset for integration into an energy minimization framework for optical flow estimation. Deep Flow efficiently handles large displacements occurring in realistic videos, and shows competitive performance on optical flow benchmarks. Furthermore, it sets a new state-of-the-art on the MPI-Sintel dataset. |
Author | Harchaoui, Zaid Weinzaepfel, Philippe Revaud, Jerome Schmid, Cordelia |
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SubjectTerms | Adaptive optics Algorithms Blends Computer vision Construction deep convolutional networks dense matching Displacement Energy management Equations Estimation Integrated optics large displacements Matching non-rigid matching Nonlinear optics Optical filters optical flow Optical imaging Polymer blends Sampling |
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Title | DeepFlow: Large Displacement Optical Flow with Deep Matching |
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