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| Zitatschlüssel | 1496Senst2016 |
|---|---|
| Autor | Tobias Senst and Jonas Geistert and Thomas Sikora |
| Buchtitel | IEEE International Conference on Image Processing |
| Seiten | 4478–4482 |
| Jahr | 2016 |
| Adresse | Phoenix, AZ, USA |
| Monat | sep |
| Notiz | IEEE Catalog Number: CFP16CIP-USB ISBN: 978-1-4673-9960-9 DOI:10.1109/ICIP.2016.7533207 |
| Verlag | IEEE |
| Zusammenfassung | 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. |