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A Database and Evaluation Methodology for Optical Flow

Baker, Simon and Roth, Stefan and Scharstein, Daniel and Black, Michael and Lewis, J. P. and Szeliski, Richard (2007):
A Database and Evaluation Methodology for Optical Flow.
IEEE Computer Society, Los Alamitos, Calif., In: IEEE 11th International Conference on Computer Vision, p. 8, [Conference or Workshop Item]

Abstract

The quantitative evaluation of optical flow algorithms by Barron et al. led to significant advances in the performance of optical flow methods. The challenges for optical flow today go beyond the datasets and evaluation methods proposed in that paper and center on problems associated with nonrigid motion, real sensor noise, complex natural scenes, and motion discontinuities. Our goal is to establish a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms. To that end, we contribute four types of data to test different aspects of optical flow algorithms: sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture; realistic synthetic sequences; high frame-rate video used to study interpolation error; and modified stereo sequences of static scenes. In addition to the average angular error used in Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and flow accuracy at motion boundaries and in textureless regions. We evaluate the performance of several well-known methods on this data to establish the current state of the art. Our database is freely available on the web together with scripts for scoring and publication of the results at http://vision.middlebury.edu/flow/.

Item Type: Conference or Workshop Item
Erschienen: 2007
Creators: Baker, Simon and Roth, Stefan and Scharstein, Daniel and Black, Michael and Lewis, J. P. and Szeliski, Richard
Title: A Database and Evaluation Methodology for Optical Flow
Language: English
Abstract:

The quantitative evaluation of optical flow algorithms by Barron et al. led to significant advances in the performance of optical flow methods. The challenges for optical flow today go beyond the datasets and evaluation methods proposed in that paper and center on problems associated with nonrigid motion, real sensor noise, complex natural scenes, and motion discontinuities. Our goal is to establish a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms. To that end, we contribute four types of data to test different aspects of optical flow algorithms: sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture; realistic synthetic sequences; high frame-rate video used to study interpolation error; and modified stereo sequences of static scenes. In addition to the average angular error used in Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and flow accuracy at motion boundaries and in textureless regions. We evaluate the performance of several well-known methods on this data to establish the current state of the art. Our database is freely available on the web together with scripts for scoring and publication of the results at http://vision.middlebury.edu/flow/.

Publisher: IEEE Computer Society, Los Alamitos, Calif.
Uncontrolled Keywords: Forschungsgruppe Visual Inference (VINF), Computer vision, Motion estimation, Flow fields, Evaluation
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: IEEE 11th International Conference on Computer Vision
Date Deposited: 16 Apr 2018 09:03
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