Title
View-Invariant Action Recognition From Point Triplets
Keywords
Action alignment; Action recognition; Homology; Pose transition; View invariance
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. © 2009 IEEE.
Publication Date
8-4-2009
Publication Title
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume
31
Issue
10
Number of Pages
1898-1905
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/TPAMI.2009.41
Copyright Status
Unknown
Socpus ID
69549135122 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/69549135122
STARS Citation
Shen, Yuping and Foroosh, Hassan, "View-Invariant Action Recognition From Point Triplets" (2009). Scopus Export 2000s. 11715.
https://stars.library.ucf.edu/scopus2000/11715