Publication:
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorHieu Pham, Huy
dc.contributor.authorKhoudour, Louahdi
dc.contributor.authorCrouzil, Alain
dc.contributor.authorZegers, Pablo
dc.contributor.authorVelastin Carroza, Sergio Alejandro
dc.date.accessioned2019-09-30T10:22:43Z
dc.date.available2019-09-30T10:22:43Z
dc.date.issued2018-09-06
dc.descriptionThis paper has been presented at : 25th IEEE International Conference on Image Processing (ICIP)en
dc.description.abstractWe propose a novel skeleton-based representation for 3D action recognition in videos using Deep Convolutional Neural Networks (D-CNNs). Two key issues have been addressed: First, how to construct a robust representation that easily captures the spatial-temporal evolutions of motions from skeleton sequences. Second, how to design D-CNNs capable of learning discriminative features from the new representation in a effective manner. To address these tasks, a skeleton-based representation, namely, SPMF (Skeleton Pose-Motion Feature) is proposed. The SPMFs are built from two of the most important properties of a human action: postures and their motions. Therefore, they are able to effectively represent complex actions. For learning and recognition tasks, we design and optimize new D-CNNs based on the idea of Inception Residual networks to predict actions from SPMFs. Our method is evaluated on two challenging datasets including MSR Action3D and NTU-RGB+D. Experimental results indicated that the proposed method surpasses state-of-the-art methods whilst requiring less computation.en
dc.format.extent5
dc.identifier.bibliographicCitationPham, H.H., Khoudour, L., Crouzil, A., Zegers, P. y Velastin, S.A. (2018). Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks. In IEEE International Conference on Image Processing.en
dc.identifier.doihttps://doi.org/10.1109/ICIP.2018.8451404
dc.identifier.isbn978-1-4799-7061-2
dc.identifier.publicationtitleIEEE International Conference on Image Processingen
dc.identifier.urihttps://hdl.handle.net/10016/28921
dc.identifier.uxxiCC/0000029977
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate7-10 October 2018en
dc.relation.eventplaceAthens, Greeceen
dc.relation.eventtitle25th IEEE International Conference on Image Processing (ICIP)en
dc.rights© 2018 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherHuman action recognitionen
dc.subject.otherSPMFen
dc.subject.otherCNNsen
dc.titleSkeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networksen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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