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Sparse motion estimation with local optical flow methods is fundamental for a wide range of computer vision application. Classical approaches like the pyramidal Lucas-Kanade method (PLK) or more sophisticated approaches like the Robust Local Optical Flow (RLOF) fail when it comes to environments with illumination changes and/or long-range motions. In this work we focus on these limitations and propose...
Optical flow methods integrating sparse point correspondences have made significant contribution in the field of optical flow estimation. Especially for the goal of estimating motion accurately and efficiently, sparse-to-dense interpolation schemes for feature point matches have shown outstanding performances. Concurrently, local optical flow methods have been significantly improved with respect to...
In this paper we present an approach for the efficient computation of optical flow fields in real-time and provide implementation details. Proposing a modification of the popular Lucas-Kanade energy functional based on integral projections allows us to speed up the method notably. We show the potential of this method which can compute dense flow fields of 640×480 pixels at a speed of 4 fps in a GPU...
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