The Community for Technology Leaders
RSS Icon
Issue No.04 - April (2010 vol.32)
pp: 579-592
Samarjit Das , Iowa State University, Ames
Our goal is to develop statistical models for the shape change of a configuration of “landmark” points (key points of interest) over time and to use these models for filtering and tracking to automatically extract landmarks, synthesis, and change detection. The term “shape activity” was introduced in recent work to denote a particular stochastic model for the dynamics of landmark shapes (dynamics after global translation, scale, and rotation effects are normalized for). In that work, only models for stationary shape sequences were proposed. But most “activities” of a set of landmarks, e.g., running, jumping, or crawling, have large shape changes with respect to initial shape and hence are nonstationary. The key contribution of this work is a novel approach to define a generative model for both 2D and 3D nonstationary landmark shape sequences. Greatly improved performance using the proposed models is demonstrated for sequentially filtering noise-corrupted landmark configurations to compute Minimum Mean Procrustes Square Error (MMPSE) estimates of the true shape and for tracking human activity videos, i.e., for using the filtering to predict the locations of the landmarks (body parts) and using this prediction for faster and more accurate landmarks extraction from the current image.
Landmark shape sequence analysis, nonstationary shape sequences, Kendall's shape space, tangent space, tracking, particle filtering.
Samarjit Das, "Nonstationary Shape Activities: Dynamic Models for Landmark Shape Change and Applications", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 4, pp. 579-592, April 2010, doi:10.1109/TPAMI.2009.94
[1] N. Vaswani and R. Chellappa, "Nonstationary Shape Activities," Proc. IEEE Conf. Decision and Control, Dec. 2005.
[2] N. Vaswani and S. Das, "Particle Filter with Efficient Importance Sampling and Mode Tracking (PF-EIS-MT) and Its Application to Landmark Shape Tracking," Proc. Asilomar Conf. Signals, Systems and Computers, 2007.
[3] D. Kendall, D. Barden, T. Carne, and H. Le, Shape and Shape Theory. John Wiley and Sons, 1999.
[4] N. Vaswani, A. RoyChowdhury, and R. Chellappa, "'Shape Activity': A Continuous State HMM for Moving/Deforming Shapes with Application to Abnormal Activity Detection," IEEE Trans. Image Processing, vol. 14, no. 10, pp. 1603-1616, Oct. 2005.
[5] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision. Cambridge Univ. Press, 2000.
[6] "CMU Motion Capture Data," http:/, 2009.
[7] I. Dryden and K. Mardia, Statistical Shape Analysis. John Wiley and Sons, 1998.
[8] T. Cootes, C. Taylor, D. Cooper, and J. Graham, "Active Shape Models: Their Training and Application," Computer Vision and Image Understanding, vol. 61, pp. 38-59, Jan. 1995.
[9] A. Veeraraghavan, A. RoyChowdhury, and R. Chellappa, "Matching Shape Sequences in Video with an Application to Human Movement Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 1896-1909, Dec. 2005.
[10] M. Isard and A. Blake, "Condensation: Conditional Density Propagation for Visual Tracking," Int'l J. Computer Vision, vol. 29, pp. 5-28, 1998.
[11] N.J. Gordon, D.J. Salmond, and A.F.M. Smith, "Novel Approach to Nonlinear/Nongaussian Bayesian State Estimation," IEE Proc.-F (Radar and Signal Processing), vol. 140, no. 2, pp. 107-113, 1993.
[12] A. Kume, I. Dryden, and H. Le, "Shape Space Smoothing Splines for Planar Landmark Data," Biometrika, vol. 94, pp. 513-528, 2007.
[13] N. Paragios, M. Jolly, M. Taron, and R. Ramaraj, "Active Shape Models and Segmentation of the Left Ventricle in Echocardiography," Proc. Fifth Int'l Conf. Scale Space and PDE Methods in Computer Vision, Apr. 2005.
[14] B. Song, N. Vaswani, and A.K. Roy-Chowdhury, "Closedloop Tracking and Change Detection in Multi-Activity Sequences," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
[15] T.F. Cootes, G. Edwards, and C.J. Taylor, "Active Appearance Models," Proc. European Conf. Computer Vision, vol. 2, pp. 484-498, 1998.
[16] B.P. Lelieveldt, R.J. van der Geest, J.H.C. Reiber, J.G. Bosch, S.C. Mitchel, and M. Sonka, "Time-Continuous Segmentation of Cardiac Image Sequences Using Active Appearance Motion Models," Proc. Int'l Conf. Information Processing in Medical Imaging, pp. 446-452, Jan. 2001.
[17] T. Tai-Peng, L. Rui, and S. Sclaroff, "Tracking Human Body on a Learned Smooth Space," Technical Report No. 2005-029, Boston Univ. Computer Science, 2005.
[18] S. Hou, A. Gatala, F. Caillette, N. Thacker, and P. Bromiley, "Real-Time Body Tracking Using a Gaussian Process Latent Variable Model," Proc. IEEE Int'l Conf. Computer Vision, Oct. 2007.
[19] A. Srivastava and E. Klassen, "Bayesian and Geometric Subspace Tracking," Advances in Applied Probability, vol. 36, pp. 43-56, Mar. 2004.
[20] S.X. Ju, M.J. Black, and Y. Yacoob, "Cardboard People: A Parameterized Model of Articulated Image Motion," Proc. Second Int'l Conf. Automatic Face and Gesture Recognition, 1996.
[21] J. Kent, "The Complex Bingham Distribution and Shape Analysis," J. Royal Statistical Soc., Series B, vol. 56, pp. 285-299, 1994.
[22] J. Shi and C. Tomasi, "Good Features to Track," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 593-600, 1994.
[23] A. Doucet, "On Sequential Monte Carlo Sampling Methods for Bayesian Filtering," Technical Report CUED/F INFENG/TR. 310, Dept. of Eng., Cambridge Univ., 1998.
[24] L. Tierney and J.B. Kadane, "Accurate Approximations for Posterior Moments and Marginal Densities," J. Am. Statistical Assoc., vol. 81, no. 393, pp. 82-86, Mar. 1986.
[25] S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A Tutorial on Particle Filters for On-Line Non-Linear/Non-Gaussian Bayesian Tracking," IEEE Trans. Signal Processing, vol. 50, no. 2, pp. 174-188, Feb. 2002.
[26] N. Vaswani, "Particle Filtering for Large Dimensional State Spaces with Multimodal Observation Likelihoods," IEEE Trans. Signal Processing, vol. 56, no. 10, pp 4583-4597, Oct. 2008.
[27] S. Khan, "Matlab Code for Optical Flow Using Lucas Kanade Method," www.cs.ucf.edukhan/, 2008.
[28] N. Vaswani, "Additive Change Detection in Nonlinear Systems with Unknown Change Parameters," IEEE Trans. Signal Processing, vol. 55, no. 3, pp. 859-872, Mar. 2007.
[29] A. Kume, I. Dryden, H.L. Le, and A. Wood, "Fitting Cubic Splines to Data in Shape Spaces of Planar Configurations," Proc. Statistics of Large Datasets, 2002.
[30] S. Das and N. Vaswani, "Model-Based Compression of Nonstationary Landmark Shape Sequences," Proc. IEEE Int'l Conf. Image Processing, 2008.
[31] N. Lawrence, "Probabilistic Non-Linear Principal Component Analysis with Gaussian Process Latent Variable Models," J. Machine Learning Research, vol. 6, pp. 1783-1816, Nov. 2005.
[32] A. Srivastava and I.H. Jermyn, "Looking for Shapes in Two-Dimensional Cluttered Point Clouds," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 9, pp. 1616-1629, Sept. 2009.
22 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool