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Silhouette-based human action recognition with a multi-class support vector machine

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorGonzález, Luis
dc.contributor.authorVelastin Carroza, Sergio Alejandro
dc.contributor.authorAcuña Leiva, Gonzalo
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderMinisterio de Educación, Cultura y Deporte (España)es
dc.date.accessioned2019-10-04T08:37:05Z
dc.date.available2019-10-04T08:37:05Z
dc.date.issued2018-05
dc.descriptionThis paper has been presented at : 9th International Conference on Pattern Recognition Systems (ICPRS 2018)en
dc.description.abstractComputer vision systems have become increasingly popular, being used to solve a wide range of problems. In this paper, a computer vision algorithm with a support vector machine (SVM) classifier is presented. The work focuses on the recognition of human actions through computer vision, using a multi-camera dataset of human actions called MuHAVi. The algorithm uses a method to extract features, based on silhouettes. The challenge is that in MuHAVi these silhouettes are noisy and in many cases include shadows. As there are many actions that need to be recognised, we take a multiclass classification ap-proach that combines binary SVM classifiers. The results are compared with previous results on the same dataset and show a significant improvement, especially for recognising actions on a different view, obtaining overall accuracy of 85.5% and of 93.5% for leave-one-camera-out and leave-one-actor-out tests respectively.en
dc.description.sponsorshipSergio A Velastin has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander.en
dc.format.extent5
dc.identifier.bibliographicCitationGonzález, L., Velastin, S.A y Acuña, G. (2018). Silhouette-based human action recognition with a multi-class support vector machine. In 9th International Conference on Pattern Recognition Systems (ICPRS 2018).en
dc.identifier.doihttps://doi.org/10.1049/cp.2018.1290
dc.identifier.isbn978-1-78561-887-1
dc.identifier.publicationtitle9th International Conference on Pattern Recognition Systems (ICPRS 2018)en
dc.identifier.urihttps://hdl.handle.net/10016/28970
dc.identifier.uxxiCC/0000030002
dc.language.isoengen
dc.publisherInstitution Of Engineering And Technology (IET)en
dc.relation.eventdate22-24 May 2018en
dc.relation.eventplaceValparaíso, Chilees
dc.relation.eventtitle9th International Conference on Pattern Recognition Systems (ICPRS 2018)en
dc.relation.projectIDGobierno de España. COFUND2013-51509es
dc.relation.projectIDGobierno de España. CEI-15-17es
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/PCOFUND-GA-2012-600371en
dc.rights© 2018 The Institution of Engineering and Technology.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherBag of key posesen
dc.subject.otherMuHAVien
dc.subject.otherSVMen
dc.subject.otherComputer visionen
dc.subject.otherHuman action recognitionen
dc.titleSilhouette-based human action recognition with a multi-class support vector machineen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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