Evaluating the performance of optical flow algorithms has been difficult because of the lack of ground truth data sets for complex scenes. We present a new method for generating motion fields from real sequences containing polyhedral objects and present a test suite for benchmarking optical flow algorithms consisting of complex synthetic sequences and real scenes with ground truth. We provide a preliminary quantitative evaluation of seven optical flow algorithms using these synthetic and real sequences. Ultimately, we feel that researchers should benchmark their own algorithms using a standard suite. To that end, we offer our Web site as a repository for standard sequences and results.
%0 Journal Article
%1 McCane2001126
%A McCane, B
%A Novins, K
%A Crannitch, D
%A Galvin, B
%D 2001
%J Computer Vision and Image Understanding
%K benchmark evaluation optical_flow
%N 1
%P 126 - 143
%R 10.1006/cviu.2001.0930
%T On Benchmarking Optical Flow
%U http://www.sciencedirect.com/science/article/pii/S1077314201909300
%V 84
%X Evaluating the performance of optical flow algorithms has been difficult because of the lack of ground truth data sets for complex scenes. We present a new method for generating motion fields from real sequences containing polyhedral objects and present a test suite for benchmarking optical flow algorithms consisting of complex synthetic sequences and real scenes with ground truth. We provide a preliminary quantitative evaluation of seven optical flow algorithms using these synthetic and real sequences. Ultimately, we feel that researchers should benchmark their own algorithms using a standard suite. To that end, we offer our Web site as a repository for standard sequences and results.
@article{McCane2001126,
abstract = {Evaluating the performance of optical flow algorithms has been difficult because of the lack of ground truth data sets for complex scenes. We present a new method for generating motion fields from real sequences containing polyhedral objects and present a test suite for benchmarking optical flow algorithms consisting of complex synthetic sequences and real scenes with ground truth. We provide a preliminary quantitative evaluation of seven optical flow algorithms using these synthetic and real sequences. Ultimately, we feel that researchers should benchmark their own algorithms using a standard suite. To that end, we offer our Web site as a repository for standard sequences and results. },
added-at = {2013-10-22T15:27:42.000+0200},
author = {McCane, B and Novins, K and Crannitch, D and Galvin, B},
biburl = {https://www.bibsonomy.org/bibtex/2e3497e26fda3b7234b143f4002bbbec4/alex_ruff},
description = {On Benchmarking Optical Flow},
doi = {10.1006/cviu.2001.0930},
interhash = {886c7f138a4e2daa31460cd248273d51},
intrahash = {e3497e26fda3b7234b143f4002bbbec4},
issn = {1077-3142},
journal = {Computer Vision and Image Understanding },
keywords = {benchmark evaluation optical_flow},
number = 1,
pages = {126 - 143},
timestamp = {2013-10-22T15:27:42.000+0200},
title = {On Benchmarking Optical Flow },
url = {http://www.sciencedirect.com/science/article/pii/S1077314201909300},
volume = 84,
year = 2001
}