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Issue No.09 - September (2010 vol.32)
pp: 1553-1567
Ioannis Patras , Queen Mary University of London, London
Edwin R. Hancock , University of York, York
ABSTRACT
This paper addresses the problem of robust template tracking in image sequences. Our work falls within the discriminative framework in which the observations at each frame yield direct probabilistic predictions of the state of the target. Our primary contribution is that we explicitly address the problem that the prediction accuracy for different observations varies, and in some cases, can be very low. To this end, we couple the predictor to a probabilistic classifier which, when trained, can determine the probability that a new observation can accurately predict the state of the target (that is, determine the “relevance” or “reliability” of the observation in question). In the particle filtering framework, we derive a recursive scheme for maintaining an approximation of the posterior probability of the state in which multiple observations can be used and their predictions moderated by their corresponding relevance. In this way, the predictions of the “relevant” observations are emphasized, while the predictions of the “irrelevant” observations are suppressed. We apply the algorithm to the problem of 2D template tracking and demonstrate that the proposed scheme outperforms classical methods for discriminative tracking both in the case of motions which are large in magnitude and also for partial occlusions.
INDEX TERMS
Regression, tracking, state estimation, relevance determination, probabilistic tracking.
CITATION
Ioannis Patras, Edwin R. Hancock, "Coupled Prediction Classification for Robust Visual Tracking", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 9, pp. 1553-1567, September 2010, doi:10.1109/TPAMI.2009.175
REFERENCES
[1] A. Agarwal and B. Triggs, "A Local Basis Representation for Estimating Human Pose from Cluttered Images," Proc. Asian Conf. Computer Vision, pp. 50-59, 2006.
[2] A. Agarwal and B. Triggs, "Recovering 3D Human Pose from Monocular Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 44-58, Jan. 2006.
[3] S. Avidan, "Support Vector Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 8, pp. 1064-1072, Aug. 2004.
[4] M. Black and A. Jepson, "Eigentracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation," Proc. European Conf. Computer Vision, pp. 329-342, Apr. 1996.
[5] A.M. Buchanan and A.W. Fitzgibbon, "Combining Local and Global Motion Models for Feature Point Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
[6] D.M.C. Sminchisescu and A. Kanaujia, "BMA$^3$ E : Discriminative Density Propagation for Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 11, pp. 2030-2044, Nov. 2007.
[7] R. Collins, Y. Liu, and M. Leordeanu, "Online Selection of Discriminative Tracking Features," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1631-1643, Oct. 2005.
[8] T. Cootes, G. Edwards, and C. Taylor, "Active Appearance Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 681-685, June 2001.
[9] J. Deutscher, A. Davison, and I. Reid, "Automatic Partitioning of High Dimensional Search Spaces Associated with Articulated Body Motion Capture," Proc. Int'l Conf. Computer Vision and Pattern Recognition, Dec. 2001.
[10] B. Horn and B. Schunck, "Determining Optical Flow," Artificial Intelligence, vol. 17, nos. 1-3, pp. 185-203, Aug. 1981.
[11] M. Isard and A. Blake, "Condensation—Conditional Density Propagation for Visual Tracking," Int'l J. Computer Vision, vol. 29, no. 1, pp. 5-28, 1998.
[12] M. Isard and A. Blake, "A Mixed-State Condensation Tracker with Automatic Model-Switching," Proc. IEEE Int'l Conf. Computer Vision, pp. 107-112, 1998.
[13] A.D. Jepson, D.J. Fleet, and T.F. El-Maraghi, "Robust Online Appearance Models for Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1296-1311, Oct. 2003.
[14] M. Jordan and R.A. Jacobs, "Hierarchical Mixtures of Experts and the Em Algorithm," Neural Computation, vol. 6, pp. 181-214, 1994.
[15] F. Jurie and M. Dhome, "Hyperplane Approximation for Template Matching," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 996-1000, July 2002.
[16] B. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," Proc. Int'l Joint Conf. Artificial Intelligence, pp. 121-130, 1981.
[17] P. Meer, D. Mintz, and A. Rosenfeld, "Robust Regression Methods for Computer Vision: A Review," Int'l J. Computer Vision, vol. 6, no. 1, pp. 59-70, 1991.
[18] K. Okuma, A. Taleghani, N.D. Freitas, J.J. Little, and D.G. Lowe, "A Boosted Particle Filter: Multitarget Detection and Tracking," Proc. European Conf. Computer Vision, pp. 28-39, May 2004.
[19] M. Pantic and I. Patras, "Dynamics of Facial Expression: Recognition of Facial Actions and Their Temporal Segments from Face Profile Image Sequences," IEEE Trans. Systems, Man, and Cybernetics, Part B, vol. 36, no. 2, pp. 433-449, Apr. 2006.
[20] I. Patras and E. Hancock, "Regression Tracking with Data Relevance Determination," Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2007.
[21] I. Patras and M. Pantic, "Particle Filtering with Factorized Likelihoods for Tracking Facial Features," Proc. IEEE Int'l Conf. Face and Gesture Recognition, pp. 97-102, May 2004.
[22] M. Pitt and N. Shephard, "Filtering via Simulation: Auxiliary Particle Filtering," J. Am. Statistical Assoc., vol. 94, no. 446, pp. 590-599, 1999.
[23] L. Sigal, S. Bhatia, S. Roth, M.J. Black, and M. Isard, "Tracking Loose-Limbed People," Proc. Int'l Conf. Computer Vision and Pattern Recognition, 2004.
[24] E. Simoncelli, E. Adelson, and D. Heeger, "Probability Distributions of Optical Flow," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 310-315, June 1991.
[25] C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas, "Discriminative Density Propagation for 3D Human Motion Estimation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[26] M. Tipping, "The Relevance Vector Machine," Advances in Neural Information Processing Systems, Morgan Kaufmann, 2000.
[27] P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 511-518, 2001.
[28] N. Vlassis and J. Verbeek, "Gaussian Mixture Learning from Noisy Data," Technical Report IAS-UVA-04-01, Informatics Inst., Univ. of Amsterdam, 2004.
[29] S. Waterhouse, D. MacKay, and T. Robinson, "Bayesian Methods for Mixtures of Experts," Advances in Neural Information Processing Systems, vol. 8, pp. 351-357, MIT Press, 1996.
[30] O. Williams, A. Blake, and R. Cipolla, "Sparse Bayesian Regression for Efficient Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1292-1304, Aug. 2005.
[31] Y. Wu, G. Hua, and T. Yu, "Switching Observation Models for Contour Tracking in Clutter," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 295-302, June 2003.
[32] K. Zimmermann, J. Matas, and T. Svoboda, "Tracking by an Optimal Sequence of Linear Predictors," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 4, pp. 677-692, Apr. 2009.
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