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Multi-view Human Action Recognition using Histograms of Oriented Gradients (HOG) Description of Motion History Images (MHIs)

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorMurtaza, Fiza
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.accessioned2019-10-02T08:14:34Z
dc.date.available2019-10-02T08:14:34Z
dc.date.issued2015-12
dc.descriptionThis paper has been presented at : 13th International Conference on Frontiers of Information Technology (FIT)en
dc.description.abstractIn this paper, a silhouette-based view-independent human action recognition scheme is proposed for multi-camera dataset. To overcome the high-dimensionality issue, incurred due to multi-camera data, the low-dimensional representation based on Motion History Image (MHI) was extracted. A single MHI is computed for each view/action video. For efficient description of MHIs Histograms of Oriented Gradients (HOG) are employed. Finally the classification of HOG based description of MHIs is based on Nearest Neighbor (NN) classifier. The proposed method does not employ feature fusion for multi-view data and therefore this method does not require a fixed number of cameras setup during training and testing stages. The proposed method is suitable for multi-view as well as single view dataset as no feature fusion is used. Experimentation results on multi-view MuHAVi-14 and MuHAVi-8 datasets give high accuracy rates of 92.65% and 99.26% respectively using Leave-One-Sequence-Out (LOSO) cross validation technique as compared to similar state-of-the-art approaches. The proposed method is computationally efficient and hence suitable for real-time action recognition systems.en
dc.description.sponsorshipS.A. Velastin acknowledges funding from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement n° 600371, el Ministerio de Economia y Competitividad (COFUND2013-51509) and Banco Santander.en
dc.format.extent6
dc.identifier.bibliographicCitationMurtaza, F., Yousaf, M.H. y Velastin, S.A. (2015). Multi-view Human Action Recognition Using Histograms of Oriented Gradients (HOG) Description of Motion History Images (MHIs). In 2015 13th International Conference on Frontiers of Information Technology (FIT), pp. 297-302.en
dc.identifier.doihttps://doi.org/10.1109/FIT.2015.59en
dc.identifier.isbn978-1-4673-9666-0/15
dc.identifier.publicationfirstpage297
dc.identifier.publicationlastpage302
dc.identifier.publicationtitle2015 13th International Conference on Frontiers of Information Technology (FIT)en
dc.identifier.urihttps://hdl.handle.net/10016/28945
dc.identifier.uxxiCC/0000024336
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate14-16 Dec. 2015en
dc.relation.eventplaceIslamabad, Pakistanen
dc.relation.eventtitle13th International Conference on Frontiers of Information Technology (FIT 2015)en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/PCOFUND-GA-2012-600371en
dc.relation.projectIDGobierno de España. COFUND 2014-51509es
dc.rights© 2015 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherAction recognitionen
dc.subject.otherHistograms of oriented gradients (Hog)en
dc.subject.otherMotion history images (Mhis)en
dc.subject.otherMuhavi dataseten
dc.titleMulti-view Human Action Recognition using Histograms of Oriented Gradients (HOG) Description of Motion History Images (MHIs)en
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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