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Sparse Aggregation Framework for Optical Flow Estimation

Abstract : We propose a sparse aggregation framework for optical flow estimation to overcome the limitations of variational methods introduced by coarse-to-fine strategies. The idea is to compute parametric motion candidates estimated in overlapping square windows of variable size taken in the semi-local neighborhood of a given point. In the second step, a sparse representation and an optimization procedure in the continuous setting are proposed to compute a motion vector close to motion candidates for each pixel. We demonstrate the feasibility and performance of our two-step approach on image pairs and compare its performances with competitive methods on the Middlebury benchmark.
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Contributor : Denis Fortun Connect in order to contact the contributor
Submitted on : Tuesday, March 31, 2015 - 9:11:22 PM
Last modification on : Thursday, January 20, 2022 - 5:32:57 PM
Long-term archiving on: : Tuesday, April 18, 2017 - 6:30:53 AM


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  • HAL Id : hal-01138012, version 1



Denis Fortun, Patrick Bouthemy, Charles Kervrann. Sparse Aggregation Framework for Optical Flow Estimation. Scale Space and Variational Methods in Computer Vision, May 2015, Lège Cap Ferret, France. ⟨hal-01138012⟩



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