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ICPR
2010
IEEE

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Human Action Recognition Using Segmented Skeletal Features

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Human Action Recognition Using Segmented Skeletal Features

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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.

Sang Min Yoon, Arjan Kuijper

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Computer Vision | Human Action Recognition | Human Actions | Human Body Parts | ICPR 2010 |

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Added	26 Jan 2011
Updated	26 Jan 2011
Type	Journal
Year	2010
Where	ICPR
Authors	Sang Min Yoon, Arjan Kuijper

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