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dc.contributor.authorJiménez Bascones, Juan Luis
dc.contributor.authorGraña Romay, Manuel María
dc.contributor.authorLópez Guede, José Manuel ORCID
dc.date.accessioned2020-01-08T13:21:13Z
dc.date.available2020-01-08T13:21:13Z
dc.date.issued2019-08-11
dc.identifier.citationNeurocomputing 353 : 96-105 (2019)es_ES
dc.identifier.issn0925-2312
dc.identifier.issn1872-8286
dc.identifier.urihttp://hdl.handle.net/10810/37526
dc.description.abstractHuman motion capture by optical sensors produces snapshots of the motion of a cloud of points that need to be labeled in order to carry out ensuing motion analysis for medical or other purposes. We generate the labeling of instantaneous captures of the cloud of points, discarding temporal correlations, in the presence of occlusions. Our approach proposes an ensemble of weak classifiers defined over geometrical features extracted from small subsets of the cloud of points. We apply an Adaboost strategy to select a minimal ensemble of weak classifiers achieving a target correct labeling detection accuracy. Furthermore, we use these features to generate the labeling of the points in the cloud even in the presence of occlusions.To deal with the occlusions of markers we search for ensembles of partial labeling solvers which can provide partial consistent labelings which cover the unoccluded markers. We test two greedy search approaches and a genetic algorithm in the search for the optimal ensemble of partial solvers We demonstrate the approach on a real dataset obtained from the measurement of gait motion of persons, with available ground truth labeling. Results are encouraging, achieving high accuracy label generation at a reduced computational cost. (C) 2019 The Author(s). Published by Elsevier B.V.es_ES
dc.description.sponsorshipThe work in this paper has been partially supported by FEDER funds for the MINECO project TIN2017-85827-P, and projects KK-2018/00071 and KK-2018/00082 of the Elkartek 2018 funding program of the Basque Government. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 777720.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/TIN2017-85827-Pes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectadaboostes_ES
dc.subjectsolver ensemblees_ES
dc.subjectpoint cloud labelinges_ES
dc.subjectmarker trackinges_ES
dc.subjectoptical motion capturees_ES
dc.subjectcapture systemes_ES
dc.subjectrecognitiones_ES
dc.titleRobust labeling of human motion markers in the presence of occlusionses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder2019 The Author(s). Published by Elsevier B.V.This is an open access article under the CCBY license. (http://creativecommons.org/licenses/by/4.0/es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0925231219303261?via%3Dihubes_ES
dc.identifier.doi10.1016/j.neucom.2018.05.132
dc.departamentoesCiencia de la computación e inteligencia artificiales_ES
dc.departamentoesIngeniería de sistemas y automáticaes_ES
dc.departamentoeuKonputazio zientziak eta adimen artifizialaes_ES
dc.departamentoeuSistemen ingeniaritza eta automatikaes_ES


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2019 The Author(s). Published by Elsevier B.V.This is an open access article under the CCBY license. (http://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's license is described as 2019 The Author(s). Published by Elsevier B.V.This is an open access article under the CCBY license. (http://creativecommons.org/licenses/by/4.0/