The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.05 - May (2009 vol.31)
pp: 919-930
Bohyung Han , Mobileye Vision Technologies, Princeton
Ying Zhu , Siemens Corporate Research, Princeton
Dorin Comaniciu , Siemens Corporate Research, Princeton
Larry S. Davis , University of Maryland - College Park, College Park
ABSTRACT
Particle filtering is frequently used for visual tracking problems since it provides a general framework for estimating and propagating probability density functions for nonlinear and non-Gaussian dynamic systems. However, this algorithm is based on a Monte Carlo approach and the cost of sampling and measurement is a problematic issue, especially for high-dimensional problems. We describe an alternative to the classical particle filter in which the underlying density function has an analytic representation for better approximation and effective propagation. The techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities with Gaussian mixtures, where all relevant parameters are automatically determined. The proposed analytic approach is shown to perform more efficiently in sampling in high-dimensional space. We apply the algorithm to real-time tracking problems and demonstrate its performance on real video sequences as well as synthetic examples.
INDEX TERMS
Bayesian filtering, density interpolation, density approximation, mean shift, density propagation, visual tracking, particle filter.
CITATION
Bohyung Han, Ying Zhu, Dorin Comaniciu, Larry S. Davis, "Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 5, pp. 919-930, May 2009, doi:10.1109/TPAMI.2008.134
REFERENCES
[1] I. Abramson, “On Bandwidth Variation in Kernel Estimates—A Square Root Law,” Annals of Statistics, vol. 10, no. 4, pp. 1217-1223, 1982.
[2] M. Adlers, “Topics in Sparse Least Squares Problems,” PhD dissertation, Linköpings Universitet, Sweden, http://www.math. liu.se/~milunthesis, 2000.
[3] 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.174-189, 2002.
[4] J. Cantarella and M. Piatek, tsnnls: A Solver for Large Sparse Least Squares Problem with Non-Negative Variables, preprint, http://www.cs.duq.edu/~piatektsnnls/, 2004.
[5] T. Cham and J. Rehg, “A Multiple Hypothesis Approach to Figure Tracking,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 219-239, 1999.
[6] C. Cheng, R. Ansari, and A. Khokhar, “Multiple Object Tracking with Kernel Particle Filter,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[7] W. Cleveland, “Robust Locally Weighted Regression and Smoothing Scatterplots,” J. Am. Statistical Assoc., vol. 74, pp. 829-836, 1979.
[8] W. Cleveland and C. Loader, “Smoothing by Local Regression: Principles and Methods,” Statistical Theory and Computational Aspects of Smoothing, pp. 10-49, 1996.
[9] D. Comaniciu, “Nonparametric Information Fusion for Motion Estimation,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 59-66, 2003.
[10] D. Comaniciu and P. Meer, “Mean Shift: A Robust Approach Toward Feature Space Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.
[11] D. Comaniciu, V. Ramesh, and P. Meer, “The Variable Bandwidth Mean Shift and Data-Driven Scale Selection,” Proc. Eighth Int'l Conf. Computer Vision, vol. 1, pp. 438-445, July 2001.
[12] J. Deutscher, A. Blake, and I. Reid, “Articulated Body Motion Capture by Annealed Particle Filtering,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2000.
[13] A. Doucet, N. de Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice. Springer Verlag, 2001.
[14] A. Doucet, S. Godsill, and C. Andrieu, “On Sequential Monte Carlo Sampling Methods for Bayesian Filtering,” Statistics and Computing, vol. 10, no. 3, pp. 197-208, 2000.
[15] B. Han, D. Comaniciu, Y. Zhu, and L.S. Davis, “Sequential Kernel Density Approximation and Its Applications to Real-Time Visual Tracking,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 7, pp. 1186-1197, July 2008.
[16] B. Han, D. Comaniciu, Y. Zhu, and L. Davis, “Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
[17] B. Han, Y. Zhu, D. Comaniciu, and L. Davis, “Kernel-Based Bayesian Filtering for Object Tracking,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2005.
[18] M. Isard and A. Blake, “Condensation—Conditional Density Propagation for Visual Tracking,” Int'l J. Computer Vision, vol. 29, no. 1, 1998.
[19] S. Julier and J. Uhlmann, “A New Extension of the Kalman Filter to Nonlinear Systems,” Proc. SPIE, vol. 3068, pp. 182-193, 1997.
[20] R.E. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Trans. Am. Soc. Mechanical Eng. D: J. Basic Eng., vol. 82, pp. 35-45, 1960.
[21] J. Kotecha and P. Djuric, “Gaussian Sum Particle Filtering,” IEEE Trans. Signal Processing, vol. 51, no. 10, pp. 2602-2612, 2003.
[22] C.L. Lauwon and B.J. Hanson, Solving Least Squares Problems. Prentice-Hall, 1974.
[23] J. MacCormick and M. Isard, “Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking,” Proc. European Conf. Computer Vision, pp. 3-19, 2000.
[24] R. Merwe, A. Doucet, N. Freitas, and E. Wan, “The Unscented Particle Filter,” Technical Report CUED/F-INFENG/TR 380, Cambridge Univ. Eng. Dept., 2000.
[25] B. Park and J. Marron, “Comparison of Data-Driven Bandwidth Selectors,” J. Am. Statistical Assoc., vol. 85, pp. 66-72, 1990.
[26] P. Perez, C. Hue, J. Vermaak, and M. Gangnet, “Color-Based Probabilistic Tracking,” Proc. European Conf. Computer Vision, vol. 1, pp. 661-675, 2002.
[27] V. Philomin, R. Duraiswami, and L.S. Davis, “Quasi-Random Sampling for Condensation,” Proc. European Conf. Computer Vision, vol. 2, pp. 134-149, 2000.
[28] T. Poggio and F. Girosi, “A Theory of Networks for Approximation and Learning,” technical report, Artificial Intelligence Laboratory, Massachusetts Inst. of Tech nology, 1989.
[29] Y. Rui and Y. Chen, “Better Proposal Distributions: Object Tracking Using Unscented Particle Filter,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 786-793, 2001.
[30] S. Sheather and M. Jones, “A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation,” J. Royal Statistical Soc. B, vol. 53, pp. 683-690, 1991.
[31] C. Sminchisescu and B. Triggs, “Covariance Scaled Sampling for Monocular 3D Body Tracking,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 447-454, 2001.
[32] C. Sminchisescu and B. Triggs, “Hyperdynamics Importance Sampling,” Proc. European Conf. Computer Vision, pp. 769-783, 2002.
[33] J. Sullivan and J. Rittscher, “Guiding Random Particles by Deterministic Search,” Proc. Eighth Int'l Conf. Computer Vision, vol. 1, pp. 323-330, 2001.
[34] P. Torma and C. Szepesvari, “Enhancing Particle Filter Using Local Likelihood Sampling,” Proc. European Conf. Computer Vision, pp. 16-27, 2004.
[35] J. Vermaak, A. Doucet, and P. Perez, “Maintaining Multi-Modality through Mixture Tracking,” Proc. Ninth Int'l Conf. Computer Vision, vol. 1, 2003.
[36] E.A. Wan and R. van der Merwe, “The Unscented Kalman Filter for Non-Linear Estimation,” Proc. Symp. 2001 Adaptive Systems for Signal Proc. Comm. and Control, 2000.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool