@INPROCEEDINGS{1496Senst2016, AUTHOR = {Tobias Senst and Jonas Geistert and Thomas Sikora}, TITLE = {Robust local optical flow: Long-range motions and varying illuminations}, BOOKTITLE = {IEEE International Conference on Image Processing}, YEAR = {2016}, MONTH = sep, PUBLISHER = {IEEE}, PAGES = {4478--4482}, ADDRESS = {Phoenix, AZ, USA}, NOTE = {IEEE Catalog Number: CFP16CIP-USB ISBN: 978-1-4673-9960-9 DOI:10.1109/ICIP.2016.7533207}, PDF = {http://elvera.nue.tu-berlin.de/files/1496Senst2016.pdf}, URL = {http://elvera.nue.tu-berlin.de/files/1496Senst2016.pdf}, 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.} }