We are analyzing https://link.springer.com/article/10.1007/s11263-010-0390-2.

Title:
A Database and Evaluation Methodology for Optical Flow | International Journal of Computer Vision
Description:
The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance. The challenges for optical flow algorithms today go beyond the datasets and evaluation methods proposed in that paper. Instead, they center on problems associated with complex natural scenes, including nonrigid motion, real sensor noise, and motion discontinuities. We propose 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: (1) sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture, (2) realistic synthetic sequences, (3) high frame-rate video used to study interpolation error, and (4) modified stereo sequences of static scenes. In addition to the average angular error used by Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and results at motion discontinuities and in textureless regions. In October 2007, we published the performance of several well-known methods on a preliminary version of our data to establish the current state of the art. We also made the data freely available on the web at http://vision.middlebury.edu/flow/ . Subsequently a number of researchers have uploaded their results to our website and published papers using the data. A significant improvement in performance has already been achieved. In this paper we analyze the results obtained to date and draw a large number of conclusions from them.
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Keywords {🔍}

google, scholar, computer, vision, flow, proceedings, conference, optical, ieee, article, pattern, motion, recognition, international, image, analysis, estimation, journal, machine, intelligence, vol, transactions, baker, black, chapter, european, data, evaluation, szeliski, methods, stereo, processing, scharstein, algorithms, computation, pock, roth, sequences, local, access, techniques, anandan, graphics, bischof, cremers, privacy, cookies, information, research, database,

Topics {✒️}

org/challenges/voc/voc2009/workshop/index high frame-rate video lucas/kanade meets horn/schunck high-accuracy motion estimation technical report uw-cse-09-08-01 optical flow detection tv-l1 optical flow duality tv-l1 flow feature-based image metamorphosis human-assisted motion annotation discontinuity-preserving variational methods piecewise-smooth flow fields direct multi-resolution estimation time-efficient optimisation framework article download pdf production-ready global illumination optical flow algorithms real-time motion computation variable-order parametric models privacy choices/manage cookies optical flow estimation fast optical flow motion boundaries based path-based method optical flow generated estimating optical flow optical flow fields benchmarking optical flow energy minimization methods computing optical flow multi-resolution flow optical flow techniques complementary optic flow related subjects frame interpolation error dense optical flow continuous energy minimization evaluation methods proposed real sensor noise component image velocity fine region-trees determining optical flow learning optical flow digital image computing ground-truth flow optical flow computation dagstuhl motion workshop plausible image interpolation including nonrigid motion estimating image motion

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Schema {🗺️}

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         description:The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance. The challenges for optical flow algorithms today go beyond the datasets and evaluation methods proposed in that paper. Instead, they center on problems associated with complex natural scenes, including nonrigid motion, real sensor noise, and motion discontinuities. We propose 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: (1) sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture, (2) realistic synthetic sequences, (3) high frame-rate video used to study interpolation error, and (4) modified stereo sequences of static scenes. In addition to the average angular error used by Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and results at motion discontinuities and in textureless regions. In October 2007, we published the performance of several well-known methods on a preliminary version of our data to establish the current state of the art. We also made the data freely available on the web at http://vision.middlebury.edu/flow/ . Subsequently a number of researchers have uploaded their results to our website and published papers using the data. A significant improvement in performance has already been achieved. In this paper we analyze the results obtained to date and draw a large number of conclusions from them.
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      headline:A Database and Evaluation Methodology for Optical Flow
      description:The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance. The challenges for optical flow algorithms today go beyond the datasets and evaluation methods proposed in that paper. Instead, they center on problems associated with complex natural scenes, including nonrigid motion, real sensor noise, and motion discontinuities. We propose 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: (1) sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture, (2) realistic synthetic sequences, (3) high frame-rate video used to study interpolation error, and (4) modified stereo sequences of static scenes. In addition to the average angular error used by Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and results at motion discontinuities and in textureless regions. In October 2007, we published the performance of several well-known methods on a preliminary version of our data to establish the current state of the art. We also made the data freely available on the web at http://vision.middlebury.edu/flow/ . Subsequently a number of researchers have uploaded their results to our website and published papers using the data. A significant improvement in performance has already been achieved. In this paper we analyze the results obtained to date and draw a large number of conclusions from them.
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         Pattern Recognition and Graphics
         Artificial Intelligence
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