This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
View-Invariant Action Recognition from Point Triplets
October 2009 (vol. 31 no. 10)
pp. 1898-1905
ASCII Text
x 
 
Yuping Shen, Hassan Foroosh, "View-Invariant Action Recognition from Point Triplets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 10, pp. 1898-1905, October, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2009.41,
author = {Yuping Shen and Hassan Foroosh},
title = {View-Invariant Action Recognition from Point Triplets},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {31},
number = {10},
issn = {0162-8828},
year = {2009},
pages = {1898-1905},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.41},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - View-Invariant Action Recognition from Point Triplets
IS - 10
SN - 0162-8828
SP1898
EP1905
EPD - 1898-1905
A1 - Yuping Shen,
A1 - Hassan Foroosh,
PY - 2009
KW - View invariance
KW - homology
KW - pose transition
KW - action recognition
KW - action alignment.
VL - 31
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Yuping Shen, University of Central Florida, Orlando
Hassan Foroosh, University of Central Florida, Orlando
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.

[1] J. Aggarwal and Q. Cai, “Human Motion Analysis: A Review,” Computer Vision and Image Understanding, vol. 73, no. 3, pp. 428-440, 1999.
[2] M. Ahmad and S. Lee, “HMM-Based Human Action Recognition Using Multiview Image Sequences,” Proc. Int'l Conf. Pattern Recognition, pp. 263-266, 2006.
[3] M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, “Actions As Space-Time Shapes,” Proc. IEEE Int'l Conf. Computer Vision, vol. 2, pp. 1395-1402, 2005.
[4] A. Bobick and J. Davis, “The Recognition of Human Movement Using Temporal Templates,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 3, pp. 257-267, Mar. 2001.
[5] F. Cuzzolin, “Using Bilinear Models for View-Invariant Action and Identity Recognition,” Proc. IEEE Computer Vision and Pattern Recognition, vol. 2, pp.1701-1708, 2006.
[6] A. Efros, A. Berg, G. Mori, and J. Malik, “Recognizing Action at a Distance,” Proc. IEEE Int'l Conf. Computer Vision, pp. 726-733, 2003.
[7] D. Gavrila, “Visual Analysis of Human Movement: A Survey,” Computer Vision and Image Understanding, vol. 73, no. 1, pp. 82-98, 1999.
[8] L.V. Gool and A. Proesmans, “Grouping and Invariants Using Planar Homologies,” Image and Vision Computing, vol. 16, pp. 21-26, 1998.
[9] A. Gritai, Y. Sheikh, and M. Shah, “On the Use of Anthropometry in the Invariant Analysis of Human Actions,” Proc. Int'l Conf. Pattern Recognition, vol. 2, 2004.
[10] R.I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, second ed. Cambridge Univ. Press, 2004.
[11] G. Johansson, “Visual Perception of Biological Motion and a Model for Its Analysis,” Perception and Psychophysics, vol. 14, pp. 201-211, 1973.
[12] I. Laptev, S. Belongie, P. Perez, and J. Wills, “Periodic Motion Detection and Segmentation via Approximate Sequence Alignment,” Proc. IEEE Int'l Conf. Computer Vision, pp. 816-823, 2005.
[13] F. Lv and R. Nevatia, “Single View Human Action Recognition Using Key Pose Matching and Viterbi Path Searching,” Proc. IEEE Computer Vision and Pattern Recognition, pp. 1-8, 2007.
[14] T. Moeslund and E. Granum, “A Survey of Computer Vision-Based Human Motion Capture,” Computer Vision and Image Understanding, vol. 81, no. 3, pp. 231-268, 2001.
[15] T. Moeslund, A. Hilton, and V. Krüger, “A Survey of Advances in Vision-Based Human Motion Capture and Analysis,” Computer Vision and Image Understanding, vol. 104, nos. 2/3, pp. 90-126, 2006.
[16] V. Parameswaran and R. Chellappa, “View Invariants for Human Action Recognition,” Proc. IEEE Computer Vision and Pattern Recognition, vol. 2, 2003.
[17] V. Parameswaran and R. Chellappa, “View Invariance for Human Action Recognition,” Int'l J. Computer Vision, vol. 66, no. 1, pp. 83-101, 2006.
[18] V. Pavlovic, J. Rehg, T. Cham, and K. Murphy, “A Dynamic Bayesian Network Approach to Figure Tracking Using Learned Dynamic Models,” Proc. IEEE Int'l Conf. Computer Vision, vol. 1, pp. 94-101, 1999.
[19] P. Pritchett and A. Zisserman, “Matching and Reconstruction from Widely Separated Views,” Proc. European Workshop 3D Structure from Multiple Images of Large-Scale Environments, pp. 78-92, 1998.
[20] D. Ramanan, D. Forsyth, and A. Zisserman, “Strike a Pose: Tracking People by Finding Stylized Poses,” Proc. IEEE Computer Vision and Pattern Recognition, vol. 1, pp. 271-278, 2005.
[21] D. Ramanan, D. Forsyth, and A. Zisserman, “Tracking People and Recognizing Their Activities,” Video Proc. Computer Vision and Pattern Recognition Conf., p. 1194, 2005.
[22] C. Rao, A. Yilmaz, and M. Shah, “View Invariant Representation and Recognition of Actions,” Int'l J. Computer Vision, vol. 50, no. 2, pp. 203-226, 2002.
[23] J. Rehg and T. Kanade, “Model-Based Tracking of Self-Occluding Articulated Objects,” Proc. Int'l Conf. Computer Vision, pp. 612-617, 1995.
[24] C. Schuldt, I. Laptev, and B. Caputo, “Recognizing Human Actions: A Local SVM Approach,” Proc. Int'l Conf. Pattern Recognition, vol. 3, 2004.
[25] J.G. Semple and G. Kneebone, Algebraic Projective Geometry. Oxford Univ. Press, 1979.
[26] Y. Sheikh and M. Shah, “Exploring the Space of a Human Action,” Proc. IEEE Int'l Conf. Computer Vision, vol. 1, 2005.
[27] J. Sullivan and S. Carlsson, “Recognizing and Tracking Human Action,” Proc. European Conf. Computer Vision, pp. 629-644, 2002.
[28] T. Syeda-Mahmood, A. Vasilescu, S. Sethi, I. Center, and C. San Jose, “Recognizing Action Events from Multiple Viewpoints,” Proc. IEEE Workshop Detection and Recognition of Events in Video, pp. 64-72, 2001.
[29] T. Viéville and Q.-T. Luong, “Motion of Points and Lines in the Uncalibrated Case,” Technical Report 2054, INRIA, 1993.
[30] L. Wang, “Abnormal Walking Gait Analysis Using Silhouette-Masked Flow Histograms,” Proc. Int'l Conf. Pattern Recognition, pp. 473-476, 2006.
[31] L. Wang et al., “Recent Developments in Human Motion Analysis,” Pattern Recognition, vol. 36, no. 3, pp. 585-601, 2003.
[32] D. Weinland, R. Ronfard, and E. Boyer, “Free Viewpoint Action Recognition Using Motion History Volumes,” Computer Vision and Image Understanding, vol. 104, nos. 2/3, pp. 249-257, 2006.
[33] A. Yilmaz and M. Shah, “Actions Sketch: A Novel Action Representation,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, 2005.
[34] A. Yilmaz and M. Shah, “Matching Actions in Presence of Camera Motion,” Computer Vision and Image Understanding, vol. 104, nos. 2/3, pp. 221-231, 2006.
[35] V. Zatsiorsky, Kinematics of Human Motion. Human Kinetics, 2002.
[36] L. Zelnik-Manor and M. Irani, “Multiview Constraints on Homographies,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp. 214-223, Feb. 2002.
[37] Z. Zhang and C. Loop, “Estimating the Fundamental Matrix by Transforming Image Points in Projective Space,” Computer Vision and Image Understanding, vol. 82, no. 2, pp. 174-180, 2001.
[38] G. Zhu, C. Xu, W. Gao, and Q. Huang, “Action Recognition in Broadcast Tennis Video Using Optical Flow and Support Vector Machine,” Lecture Notes in Computer Science, vol. 3979, pp. 89-98, Springer, 2006.

Index Terms:
View invariance, homology, pose transition, action recognition, action alignment.
Citation:
Yuping Shen, Hassan Foroosh, "View-Invariant Action Recognition from Point Triplets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 10, pp. 1898-1905, Oct. 2009, doi:10.1109/TPAMI.2009.41
Usage of this product signifies your acceptance of the Terms of Use.