A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories

Missing marker information is a common problem in Motion Capture (MoCap) systems. Commercial MoCap software provides several methods for reconstructing incomplete marker trajectories; however, these methods still rely on manual intervention. Current alternatives proposed in the literature still pres...

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Autores principales:	Diana Gomes, Vânia Guimarães, Joana Silva
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	gap filling Kalman filter missing markers motion capture optimization Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/0d76e7149eb8407d8ada2f729b49c929
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spelling	oai:doaj.org-article:0d76e7149eb8407d8ada2f729b49c9292021-11-11T14:58:52ZA Fully-Automatic Gap Filling Approach for Motion Capture Trajectories10.3390/app112198472076-3417https://doaj.org/article/0d76e7149eb8407d8ada2f729b49c9292021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9847https://doaj.org/toc/2076-3417Missing marker information is a common problem in Motion Capture (MoCap) systems. Commercial MoCap software provides several methods for reconstructing incomplete marker trajectories; however, these methods still rely on manual intervention. Current alternatives proposed in the literature still present drawbacks that prevent their widespread adoption. The lack of fully automated and universal solutions for gap filling is still a reality. We propose an automatic frame-wise gap filling routine that simultaneously explores restrictions between markers’ distance and markers’ dynamics in a least-squares minimization problem. This algorithm constitutes the main contribution of our work by simultaneously overcoming several limitations of previous methods that include not requiring manual intervention, prior training or training data; not requiring information about the skeleton or a dedicated calibration trial and by being able to reconstruct all gaps, even if these are located in the initial and final frames of a trajectory. We tested our approach in a set of artificially generated gaps, using the full body marker set, and compared the results with three methods available in commercial MoCap software: spline, pattern and rigid body fill. Our method achieved the best overall performance, presenting lower reconstruction errors in all tested conditions.Diana GomesVânia GuimarãesJoana SilvaMDPI AGarticlegap fillingKalman filtermissing markersmotion captureoptimizationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9847, p 9847 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gap filling Kalman filter missing markers motion capture optimization Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	gap filling Kalman filter missing markers motion capture optimization Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Diana Gomes Vânia Guimarães Joana Silva A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
description	Missing marker information is a common problem in Motion Capture (MoCap) systems. Commercial MoCap software provides several methods for reconstructing incomplete marker trajectories; however, these methods still rely on manual intervention. Current alternatives proposed in the literature still present drawbacks that prevent their widespread adoption. The lack of fully automated and universal solutions for gap filling is still a reality. We propose an automatic frame-wise gap filling routine that simultaneously explores restrictions between markers’ distance and markers’ dynamics in a least-squares minimization problem. This algorithm constitutes the main contribution of our work by simultaneously overcoming several limitations of previous methods that include not requiring manual intervention, prior training or training data; not requiring information about the skeleton or a dedicated calibration trial and by being able to reconstruct all gaps, even if these are located in the initial and final frames of a trajectory. We tested our approach in a set of artificially generated gaps, using the full body marker set, and compared the results with three methods available in commercial MoCap software: spline, pattern and rigid body fill. Our method achieved the best overall performance, presenting lower reconstruction errors in all tested conditions.
format	article
author	Diana Gomes Vânia Guimarães Joana Silva
author_facet	Diana Gomes Vânia Guimarães Joana Silva
author_sort	Diana Gomes
title	A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
title_short	A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
title_full	A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
title_fullStr	A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
title_full_unstemmed	A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
title_sort	fully-automatic gap filling approach for motion capture trajectories
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0d76e7149eb8407d8ada2f729b49c929
work_keys_str_mv	AT dianagomes afullyautomaticgapfillingapproachformotioncapturetrajectories AT vaniaguimaraes afullyautomaticgapfillingapproachformotioncapturetrajectories AT joanasilva afullyautomaticgapfillingapproachformotioncapturetrajectories AT dianagomes fullyautomaticgapfillingapproachformotioncapturetrajectories AT vaniaguimaraes fullyautomaticgapfillingapproachformotioncapturetrajectories AT joanasilva fullyautomaticgapfillingapproachformotioncapturetrajectories
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A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories

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