Yoon, Sang Min ; Kuijper, Arjan (2013)
Human Action Recognition Based on Skeleton Splitting.
In: Expert Systems with Applications, 40 (17)
doi: 10.1016/j.eswa.2013.06.024
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Human action recognition, defined as the understanding of the human basic actions from video streams, has a long history in the area of computer vision and pattern recognition because it can be used for various applications. We propose a novel human action recognition methodology by extracting the human skeletal features and separating them into several human body parts such as face, torso, and limbs to efficiently visualize and analyze the motion of human body parts. Our proposed human action recognition system consists of two steps: (i) automatic skeletal feature extraction and splitting by measuring the similarity between neighbor pixels in the space of diffusion tensor fields, and (ii) human action recognition by using multiple kernel based Support Vector Machine. Experimental results on a set of test database show that our proposed method is very efficient and effective to recognize the actions using few parameters.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2013 |
| Autor(en): | Yoon, Sang Min ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Human Action Recognition Based on Skeleton Splitting |
| Sprache: | Englisch |
| Publikationsjahr: | 2013 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Expert Systems with Applications |
| Jahrgang/Volume einer Zeitschrift: | 40 |
| (Heft-)Nummer: | 17 |
| DOI: | 10.1016/j.eswa.2013.06.024 |
| Kurzbeschreibung (Abstract): | Human action recognition, defined as the understanding of the human basic actions from video streams, has a long history in the area of computer vision and pattern recognition because it can be used for various applications. We propose a novel human action recognition methodology by extracting the human skeletal features and separating them into several human body parts such as face, torso, and limbs to efficiently visualize and analyze the motion of human body parts. Our proposed human action recognition system consists of two steps: (i) automatic skeletal feature extraction and splitting by measuring the similarity between neighbor pixels in the space of diffusion tensor fields, and (ii) human action recognition by using multiple kernel based Support Vector Machine. Experimental results on a set of test database show that our proposed method is very efficient and effective to recognize the actions using few parameters. |
| Freie Schlagworte: | Human action recognition, Diffusion tensor fields, Skeletons |
| 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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