Research article summary (published 29 Sep 2009):

View-invariant action recognition from point triplets.

Full 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.

Author information

Author/s: Shen, Yuping (Y); Foroosh, Hassan (H);

Affiliation: University of Central Florida, Orlando, FL 32816-2362, USA. ypshen(-atsign-)cs.ucf.edu

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2009-Oct; vol 31 (issue 10) : pp 1898-905

Dates: Created 2009/08/21; Completed 2009/10/06;

PMID: 19696457, status: MEDLINE (last retrieval date: 10/6/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms Databases, Factual Human Activities Humans

Motion Movement - physiology Pattern Recognition, Automated - methods Posture - physiology

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