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Human Action Recognition Using Segmented Skeletal Features

Yoon, Sang Min ; Kuijper, Arjan (2010)
Human Action Recognition Using Segmented Skeletal Features.
20th International Conference on Pattern Recognition. Proceedings.
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

We present a novel human action recognition system based on segmented skeletal features which are separated into several human body parts such as face, torso and limbs. Our proposed human action recognition system consists of two steps: (i) automatic skeletal feature extraction and splitting by measuring the similarity in the space of diffusion tensor fields, and (ii) multiple kernel Support Vector Machine based human action recognition. Experimental results on a set of test database show that our proposed method is very efficient and effective to recognize human actions using few parameters, independent of dimensions, shadows, and viewpoints.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2010
Autor(en): Yoon, Sang Min ; Kuijper, Arjan
Art des Eintrags: Bibliographie
Titel: Human Action Recognition Using Segmented Skeletal Features
Sprache: Englisch
Publikationsjahr: 2010
Verlag: IEEE Computer Society Conference Publishing Services (CPS), Los Alamitos, Calif.
Veranstaltungstitel: 20th International Conference on Pattern Recognition. Proceedings
Kurzbeschreibung (Abstract):

We present a novel human action recognition system based on segmented skeletal features which are separated into several human body parts such as face, torso and limbs. Our proposed human action recognition system consists of two steps: (i) automatic skeletal feature extraction and splitting by measuring the similarity in the space of diffusion tensor fields, and (ii) multiple kernel Support Vector Machine based human action recognition. Experimental results on a set of test database show that our proposed method is very efficient and effective to recognize human actions using few parameters, independent of dimensions, shadows, and viewpoints.

Freie Schlagworte: Similarity measures, Diffusion tensor fields, Markerless tracking, Human action recognition
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
Hinterlegungsdatum: 12 Nov 2018 11:16
Letzte Änderung: 12 Nov 2018 11:16
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