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Issue No.12 - December (2009 vol.31)
pp: 2196-2210
Qian Yu , University of Southern California, Los Angeles
We propose a framework for tracking multiple targets, where the input is a set of candidate regions in each frame, as obtained from a state-of-the-art background segmentation module, and the goal is to recover trajectories of targets over time. Due to occlusions by targets and static objects, as also by noisy segmentation and false alarms, one foreground region may not correspond to one target faithfully. Therefore, the one-to-one assumption used in most data association algorithms is not always satisfied. Our method overcomes the one-to-one assumption by formulating the visual tracking problem in terms of finding the best spatial and temporal association of observations, which maximizes the consistency of both motion and appearance of trajectories. To avoid enumerating all possible solutions, we take a Data-Driven Markov Chain Monte Carlo (DD-MCMC) approach to sample the solution space efficiently. The sampling is driven by an informed proposal scheme controlled by a joint probability model combining motion and appearance. Comparative experiments with quantitative evaluations are provided.
Multiple-target tracking, data association, MCMC, visual surveillance.
Qian Yu, "Multiple-Target Tracking by Spatiotemporal Monte Carlo Markov Chain Data Association", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 12, pp. 2196-2210, December 2009, doi:10.1109/TPAMI.2008.253
[1] http:/, 2009.
[2] P. Green, “Trans-Dimensional Markov Chain Monte Carlo,” Highly Structured Stochastic Systems, Oxford Univ. Press, 2003.
[3] D. Reid, “An Algorithm for Tracking Multiple Targets,” IEEE Trans. Automatic Control, vol. 24, no. 6, pp. 84-90, Dec. 1979.
[4] P.S. Maybeck, Stochastic Models, Estimation, and Control. Academic Press, Inc., 1979.
[5] P.J. Green, Trans-Dimensional Markov Chain Monte Carlo. Oxford Univ. Press, 2003.
[6] A. Yilmaz, O. Javed, and M. Shah, “Object Tracking: A Survey,” ACM Computing Surveys, vol. 38, 2006.
[7] T. Yu and Y. Wu, “Collaborative Tracking of Multiple Targets,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 834-841, 2004.
[8] R.T. Collins, “Mean-Shift Blob Tracking through Scale Space,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 234-240, 2003.
[9] T. Zhao and R. Nevatia, “Tracking Multiple Humans in Crowded Environment,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 406-413, 2004.
[10] J. Kang, I. Cohen, and G. Medioni, “Continuous Tracking within and across Camera Streams,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 267-272, June 2003.
[11] C. Morefield, “Application of 0-1 Integer Programming to Multitarget Tracking Problems,” IEEE Trans. Automatic Control, vol. 22, no. 3, pp. 302-312, June 1971.
[12] Z. Khan, T. Balch, and F. Dellaert, “Multitarget Tracking with Split and Merged Measurements,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 605-610, 2005.
[13] I. Cox and S. Hingorani, “An Efficient Implementation of Reid's MHT Algorithm and Its Evaluation for the Purpose of Visual Tracking,” Proc. Int'l Conf. Pattern Recognition, pp. 437-443, 1994.
[14] T. Fortman, Y. Bar-Shalom, and M. Scheffe, “Sonar Tracking of Multiple Targets Using Joint Probabilistic Data Association,” IEEE J. Oceanic Eng., vol. OE-8, no. 3, pp. 173-184, July 1983.
[15] S. Oh, S. Russell, and S. Sastry, “Markov Chain Monte Carlo Data Association for General Multiple-Target Tracking Problems,” Proc. 43rd IEEE Conf. Decision and Control, 2004.
[16] Z. Khan, T. Balch, and F. Dellaert, “An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets,” Proc. European Conf. Computer Vision, pp. 279-290, 2004.
[17] Z. Khan, T. Balch, and F. Dellaert, “MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 11, pp. 1805-1819, Nov. 2005.
[18] Z. Tu and S. Zhu, “Image Segmentation by Data Driven Markov Chain Monte Carlo,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 657-673, May 2002.
[19] A.B. Poore, “Multidimensional Assignment Formulation of Data Association Problems Arising from Multitarget and Multisensor Tracking,” Computational Optimization and Applications, vol. 3, pp. 27-57, 1994.
[20] Y. Bar-Shalom, T. Fortmann, and M. Scheffe, “Joint Probabilistic Data Association for Multiple Targets in Clutter,” Proc. Conf. Information Sciences and Systems, 1980.
[21] K. Smith, D. Gatica-Perez, and J.-M. Odobez, “Using Particles to Track Varying Numbers of Interacting People,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 962-969, 2005.
[22] A. Mittal and L. Davis, “M2tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene,” Int'l J. Computer Vision, vol. 51, pp. 189-203, 2003.
[23] F. Dellaert, S.M. Seitz, C.E. Thorpe, and S. Thrun, “Structure from Motion without Correspondence,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2000.
[24] S. Cong, L. Hong, and D. Wicker, “Markov Chain Monte Carlo Approach for Association Probability Evaluation,” Proc. IEE— Control Theory and Applications, vol. 151, no. 2, pp. 185-193, Mar. 2004.
[25] P. Green, “Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination,” Biometrika, vol. 82, pp. 711-732, 1995.
[26] P.S.R. Kasturi, D. Goldgof, and V. Manohar, “Performance Evaluation Protocol for Text and Face Detection and Tracking in Video Analysis and Content Extraction (VACE-II),” technical report, Univ. of South Florida, 2004.
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