Title

View-Invariant Action Recognition from Point Triplets

Authors

Authors

Y. P. Shen;H. Foroosh

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

IEEE Trans. Pattern Anal. Mach. Intell.

Keywords

View invariance; homology; pose transition; action recognition; action; alignment; HUMAN MOTION CAPTURE; HUMAN MOVEMENT; FLOW; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

Abstract

We propose a new view-invariant measure for action recognition. For this purpose, we introduce the idea that the motion of an articulated body can be decomposed into rigid motions of planes defined by triplets of body points. Using the fact that the homography induced by the motion of a triplet of body points in two identical pose transitions reduces to the special case of a homology, we use the equality of two of its eigenvalues as a measure of the similarity of the pose transitions between two subjects, observed by different perspective cameras and from different viewpoints. Experimental results show that our method can accurately identify human pose transitions and actions even when they include dynamic timeline maps, and are obtained from totally different viewpoints with different unknown camera parameters.

Journal Title

Ieee Transactions on Pattern Analysis and Machine Intelligence

Volume

31

Issue/Number

10

Publication Date

1-1-2009

Document Type

Article

Language

English

First Page

1898

Last Page

1905

WOS Identifier

WOS:000268996500013

ISSN

0162-8828

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