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Clustering of human actions using invariant body shape descriptor and dynamic time warping | IEEE Conference Publication | IEEE Xplore

Clustering of human actions using invariant body shape descriptor and dynamic time warping


Abstract:

We propose a human action clustering method based on a 3D representation of the body in terms of volumetric coordinates. Features representing body postures are extracted...Show More

Abstract:

We propose a human action clustering method based on a 3D representation of the body in terms of volumetric coordinates. Features representing body postures are extracted directly from 3D data, making the system inherently insensitive to viewpoint dependence, motion ambiguities and self-occlusions. An invariant shape descriptor of human body is obtained in order to capture only posture-dependent characteristics, despite possible differences in translation, orientation, scale and body size. Frame-by-frame descriptions, generated from a gesture sequence, are collected together in matrices. Clustering of action matrices is eventually performed, and through a dynamic time warping (while computing the distance metric), we gain independence from possible temporal nonlinear distortions among different instances of the same gesture.
Date of Conference: 15-16 September 2005
Date Added to IEEE Xplore: 23 January 2006
Print ISBN:0-7803-9385-6
Conference Location: Como, Italy

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