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Recovering Articulated Pose: A Comparison of Two Pre and Postimposed Constraint Methods
January 2006 (vol. 28 no. 1)
pp. 163-168
We contrast the performance of two methods of imposing constraints during the tracking of articulated objects, the first method preimposing the kinematic constraints during tracking and, thus, using the minimum degrees of freedom, and the second imposing constraints after tracking and, hence, using the maximum. Despite their very different formulations, the methods recover the same pose change. Further comparisons are drawn in terms of computational speed and algorithmic simplicity and robustness, and it is the last area which is the most telling. The results suggest that using built-in constraints is well-suited to tracking individual articulated objects, whereas applying constraints afterward is most suited to problems involving contact and breakage between articulated (or rigid) objects, where the ability to test tracking performance quickly with constraints turned on or off is desirable.

[1] D.C. Hogg, “Model-Based Vision: A Program to See a Walking Person,” Image and Vision Computing, vol. 1, no. 1, pp. 5-20, 1983.
[2] C. Bregler and J. Malik, “Tracking People with Twists and Exponential Maps,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 8-15, 1998.
[3] H. Sidenbladh, M.J. Black, and D.J. Fleet, “Stochastic Tracking of 3D Humand Figures Using 2D Image Motion,” Proc. Sixth European Conf. Computer Vision, pp. 702-718, 2000.
[4] S.X. Ju, M.J. Black, and Y. Yacoob, “Cardboard People: A Parametrized Model of Articulated Motion,” Proc. Second Int'l Conf. Automatic Face and Gesture Recognition, pp. 38-44, 1996.
[5] D.M. Gavrila and L.S. Davis, “3D Model-Based Tracking of Humans in Action: A Multiview Approach,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 73-80, 1996.
[6] B. Stenger, P.R.S. Mendonca, and R. Cipolla, “Model-Based 3D Tracking of an Articulated Hand,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 310-315, 2001.
[7] T. Drummond and R. Cipolla, “Real-Time Tracking Highly Articulated Structures in the Presence of Noisy Measurements,” Proc Eighth Int'l Conf. Computer Vision, 2001.
[8] T. Heap and D. Hogg, “Towards 3D Hand Tracking Using a Deformable Model,” Proc. Second Conf. Face and Gesture Recognition, pp. 140-145, Oct. 1996.
[9] J. Deutscher, A. Blake, and I.D. Reid, “Articulated Body Motion Capture by Annealed Particle Filtering,” Proc. Conf. Computer Vision and Pattern Recognition, pp. 2126-2133, 2000.
[10] T. Drummond and R. Cipolla, “Real-Time Visual Tracking of Complex Structures,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 932-946, July 2002.
[11] M. Brand, “Shadow Puppetry,” Proc. Seventh IEEE Int'l Conf. Computer Vision, pp. 1237-1244, 1999.
[12] B.J. Tordoff, W.W. Mayol, T.E. de Campos, and D.W. Murray, “Head Pose Estimation for Wearable Robot Control,” Proc. British Machine Vision Conf., pp. 807-816, 2002.
[13] Y. Yacoob and L.S. Davis, “Learned Temporal Models of Image Motion,” Proc. Sixth IEEE Int'l Conf. Computer Vision, pp. 446-453, 1998.
[14] S. Wachter and H-H. Nagel, “Tracking Persons in Monocular Image Sequences,” Computer Vision and Image Understanding, vol. 74, no. 3, pp. 174-192, 1999.
[15] M. Isard and A. Blake, “Contour Tracking by Stochastic Propagation of Conditional Density,” Proc. Fourth European Conf. Computer Vision, pp. 343-356, 1996.
[16] C. Sminchisescu and W. Triggs, “Covariance Scaled Sampling for Monocular 3D Body Tracking,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 447-454, 2001.
[17] J.M. Rehg, D.D. Morris, and T. Kanade, “Ambiguities in Visual Tracking of Articulated Objects Using Two- and Three-Dimensional Models,” Int'l J. Robotics Research, vol. 22, no. 6, pp. 393-418, 2003.
[18] T. Drummond and R. Cipolla, “Real-Time Tracking of Multiple Articulated Structures in Multiple Views,” Proc. Sixth European Conf. Computer Vision, pp. 20-36, 2000.
[19] Y. Wu, G. Hua, and T. Yu, “Tracking Articulated Body by Dynamic Markov Network,” Proc. Ninth Int'l Conf Computer Vision, pp. 1094-1101, 2003.
[20] Y. Hel-Or and M. Werman, “Constraint Fusion for Recognition and Localization of Articulated Objects,” Int'l J. Computer Vision, vol. 19, no. 1, pp. 15-28, July 1996.
[21] K. Nickels and S. Hutchinson, “Model-Based Tracking of Complex Articulated Objects,” IEEE Trans. Robotics and Automation, vol. 17, no. 1, pp. 28-36, 2001.
[22] J.M. Rehg and T. Kanade, “Model-Based Tracking of Self-Occluding Articulated Objects,” Proc. Fifth IEEE Int'l Conf. Computer Vision, pp. 612-617, 1995.
[23] C. Harris, “Tracking with Rigid Models,” Active Vision, A. Blake and A. Yuille, eds., pp. 59-73, Cambridge, Mass.: MIT Press, 1992.
[24] T.E. de Campos, B.J. Tordoff, and D.W. Murray, “Linear Recovery of Articulated Pose Change: Comparing Pre- and Post-Imposed Constraints,” Technical Report OUEL 2279/05, Dept. of Eng. Science, Univ. of Oxford, 2005.
[25] B.J. Tordoff and D.W. Murray, “Guided Sampling and Consensus for Motion Estimation,” Proc. Seventh European Conf. Computer Vision, pp. 82-96, 2002.

Index Terms:
Index Terms- Visual tracking, articulated objects, motion constraints.
Citation:
Teofilo E. de Campos, Ben J. Tordoff, David W. Murray, "Recovering Articulated Pose: A Comparison of Two Pre and Postimposed Constraint Methods," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 163-168, Jan. 2006, doi:10.1109/TPAMI.2006.22
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