CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2009 vol.31 Issue No.10 - October
Issue No.10 - October (2009 vol.31)
Yuping Shen , University of Central Florida, Orlando
Hassan Foroosh , University of Central Florida, Orlando
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.41
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.
View invariance, homology, pose transition, action recognition, action alignment.
Yuping Shen, Hassan Foroosh, "View-Invariant Action Recognition from Point Triplets", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 10, pp. 1898-1905, October 2009, doi:10.1109/TPAMI.2009.41