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Inter and Intra Class Correlation Analysis (IIcCA) for Human Action Recognition in Realistic Scenarios

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorNazir, Saima
dc.contributor.authorYousaf, Muhammad Haroon
dc.contributor.authorVelastin Carroza, Sergio Alejandro
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.accessioned2019-10-01T08:23:03Z
dc.date.available2019-10-01T08:23:03Z
dc.date.issued2017-07-11
dc.descriptionThis paper has been presented at : 8th International Conference of Pattern Recognition Systems (ICPRS 2017)en
dc.description.abstractHuman action recognition in realistic scenarios is an important yet challenging task. In this paper we propose a new method, Inter and Intra class correlation analysis (IICCA), to handle inter and intra class variations observed in realistic scenarios. Our contribution includes learning a class specific visual representation that efficiently represents a particular action class and has a high discriminative power with respect to other action classes. We use statistical measures to extract visual words that are highly intra correlated and less inter correlated. We evaluated and compared our approach with state-of-the-art work using a realistic benchmark human action recognition dataset.en
dc.description.sponsorshipS.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, the Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) the Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander.en
dc.format.extent6
dc.identifier.bibliographicCitationNazir, S., Yousaf, M.H y Velastin, S.A. (2017). Inter and Intra class correlation analysis (IIcCA) for human action recognition in realistic scenarios. In 8th International Conference of Pattern Recognition Systems (ICPRS 2017)en
dc.identifier.doihttps://doi.org/10.1049/cp.2017.0149
dc.identifier.isbn978-1-78561-652-5
dc.identifier.publicationtitle8th International Conference of Pattern Recognition Systems (ICPRS 2017)en
dc.identifier.urihttps://hdl.handle.net/10016/28931
dc.identifier.uxxiCC/0000027462
dc.language.isoengen
dc.publisherThe Institution Of Engineering And Technologyen
dc.relation.eventdate11-13 July 2017en
dc.relation.eventplaceMadrid,Españaes
dc.relation.eventtitle8th International Conference of Pattern Recognition Systems (ICPRS 2017)en
dc.relation.projectIDGobierno de España. COFUND2013-51509es
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/600371en
dc.relation.projectIDGobierno de España. CEI-15-17es
dc.rights© 2017 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherHuman action recognitionen
dc.subject.otherInter and intra class variationen
dc.subject.otherCorrelation analysisen
dc.subject.otherUCF Sportsen
dc.titleInter and Intra Class Correlation Analysis (IIcCA) for Human Action Recognition in Realistic Scenariosen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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