@INPROCEEDINGS{1498Geistert2016,
	AUTHOR = {Jonas Geistert and Tobias Senst and Thomas Sikora},
	TITLE = {Robust Local Optical Flow: Dense Motion Vector Field Interpolation},
	BOOKTITLE = {Picture Coding Symposium},
	YEAR = {2016},
	MONTH = dec,
	PUBLISHER = {IEEE},
	PAGES = {1--5},
	ADDRESS = {Nuremberg, Germany},
	NOTE = {In IEEE-Explore zugefügt am 24 April 2017!
Electronic ISSN: 2472-7822 
DOI:  10.1109/PCS.2016.7906352},
	PDF = {http://elvera.nue.tu-berlin.de/files/1498Geistert2016.pdf},
	ABSTRACT = {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 long-range motion estimation in environments with varying illumination. This motivates us to propose a sparse-to-dense approach based on the Robust Local Optical Flow method. We study the performance of different efficient motion vector interpolation techniques for recent optical low benchmarks. Compared state-of-the-art method the proposed approach is significantly faster while remaining competitive accuracy on Middlebury, KITTI 2015 and MPI-Sintel data-set.}
}