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Feature Similarity and Frequency-Based Weighted Visual Words Codebook Learning Scheme for Human Action Recognition

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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.date.accessioned2019-10-01T08:57:22Z
dc.date.available2019-10-01T08:57:22Z
dc.date.issued2017-11-20
dc.descriptionThis paper has been presented at : 8th Pacific-Rim Symposium, PSIVT 2017.en
dc.description.abstractHuman action recognition has become a popular field for computer vision researchers in the recent decade. This paper presents a human action recognition scheme based on a textual information concept inspired by document retrieval systems. Videos are represented using a commonly used local feature representation. In addition, we formulate a new weighted class specific dictionary learning scheme to reflect the importance of visual words for a particular action class. Weighted class specific dictionary learning enriches the scheme to learn a sparse representation for a particular action class. To evaluate our scheme on realistic and complex scenarios, we have tested it on UCF Sports and UCF11 benchmark datasets. This paper reports experimental results that outperform recent state-of-the-art methods for the UCF Sports and the UCF11 dataset i.e. 98.93% and 93.88% in terms of average accuracy respectively. To the best of our knowledge, this contribution is first to apply a weighted class specific dictionary learning method on realistic human action recognition datasets.en
dc.description.sponsorshipSergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement n 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. Authors also acknowledges support from the Directorate of ASR and TD, University of Engineering and Technology Taxila, Pakistan.en
dc.format.extent11
dc.identifier.bibliographicCitationNazir, S., Yousaf, M.H. y Velastin, S.A. (2018). Feature Similarity and Frequency-Based Weighted Visual Words Codebook Learning Scheme for Human Action Recognition. In PSIVT 2017 Image and Video Technology,10749, pp. 326-336.en
dc.identifier.isbn978-3-319-75786-5
dc.identifier.publicationfirstpage326
dc.identifier.publicationlastpage336
dc.identifier.publicationtitleImage and Video Technology PSIVT 2017en
dc.identifier.publicationvolume10749
dc.identifier.urihttps://hdl.handle.net/10016/28933
dc.identifier.uxxiCC/0000029176
dc.language.isoengen
dc.publisherSpringeren
dc.relation.eventdate20-24 November 2017en
dc.relation.eventplaceWuhan, Chinaen
dc.relation.eventtitle8th Pacific-Rim Symposium on Image and Video Technology (PSIVT)en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/PCOFUND-GA-2012-600371en
dc.relation.projectIDGobierno de España. COFUND2013-51509es
dc.rights© Springer International Publishing AG, part of Springer Nature 2018en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherHuman action recognitionen
dc.subject.otherBag of visual wordsen
dc.subject.otherSpatio-temporal featuresen
dc.subject.otherUcf sportsen
dc.titleFeature Similarity and Frequency-Based Weighted Visual Words Codebook Learning Scheme for Human Action Recognitionen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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