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Robust local optical flow: Long-range motions and varying illuminations
Citation key 1496Senst2016
Author Tobias Senst and Jonas Geistert and Thomas Sikora
Title of Book IEEE International Conference on Image Processing
Pages 4478–4482
Year 2016
Address Phoenix, AZ, USA
Month sep
Note IEEE Catalog Number: CFP16CIP-USB ISBN: 978-1-4673-9960-9 DOI:10.1109/ICIP.2016.7533207
Publisher IEEE
Abstract 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 a novel local optical flow framework taking into account an illumination model to deal with varying illumination and a prediction step based on a perspective global motion model to deal with long-range motions. Experimental results shows tremendous improvements, e.g. 56% smaller error for dense motion fields on the KITTI and an about 76% smaller error for sparse motion fields on the Sintel dataset.
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