Student-t mixture filter for robust, real-time visual tracking

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5304 LNCS, Issue PART 3, 2008, Pages 372-385

  Loxam, James ^a   Drummond, Tom ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; INFORMATION FILTERING; INFORMATION THEORY; MIXTURES;

BAYESIAN; HYBRID FILTERS; NOISY MEASUREMENTS; REAL TIME; REAL-TIME OPERATION; VISUAL TRACKING;

KALMAN FILTERS;

EID: 56749159829     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88690-7_28     Document Type: Conference Paper

References (20)

1. Huber, P.: Robust Statistics. Wiley, New York (1981)
2. Kalman, R.: A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering 82, 35-45 (1960)
3. Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381-395 (1981)
4. Finetti, B.D.: The bayesian approach to the rejection of outliers. In: Proc. Fourth Berkeley Symp. on Math. Statist. and Prob., vol. 1, pp. 199-210 (1961)
5. Gordon, N.: A hybrid bootstrap filter for target tracking in clutter. IEEE Transactions on Aerospace and Electronic Systems (1997)
6. Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5-28 (1998)
7. Gordon, N., Smith, A.: Approximate non-gaussian bayesian estimation and modal consistency. Journal of the Royal Statistical Society B 55(4), 913-918 (1993)
8. van der Merwe, R., de Freitas, J., Doucet, A., Wan, E.: The unscented particle filter. In: Advances in Neural Information Processing Systems, vol. 13 (2001)
9. Kotecha, J.H., Djuric, P.M.: Gaussian sum particle filtering. IEEE Transactions on Signal Processing 51, 2602-2612 (2003)
10. Han, B., Zhu, Y., Comaniciu, D., Davis, L.: Kernel-based bayesian filtering for object tracking. In: Proc. IEEE CVPR (2005)
11. Li, S., Wang, H., Chai, T.: A t-distribution based particle filter for target tracking. In: Proc. American Control Conference, pp. 2191-2196 (2006)
12. Dawid, A.P.: Posterior expectations for large observations. Miscellanea, 664-667 (1973)
13. O'Hagan, A.: On outlier rejection phenomena in bayes inference. Journal of the Royal Statistical Society B 41(3), 358-367 (1979)
14. West, M.: Robust sequential approximate bayesian estimation. Journal of the Royal Statistical Society B 43(2), 157-166 (1981)
15. Sorenson, H., Alspach, D.: Recursive Bayesian estimation using Gaussian sums. Automatica 7(4), 465-479 (1971)
16. Meinhold, R.J., Singpurwalla, N.D.: Robustification of kalman filter models. Journal of the American Statistical Association 84, 479-486 (1989)
17. Mardia, K.: Measures of multivariate skewness and kurtosis with applications. Biometrika 57, 519-530 (1970)
18. Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: SODA 2007. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Philadelphia, PA, USA, pp. 1027-1035. Society for Industrial and Applied Mathematics (2007)
19. Jammalamadaka, S.R., Rao, T.S., Terdik, G.: Higher order cumulants of random vectors, differential operators, and applications to statistical inference and time series (1991)
20. Rosten, E., Drummond, T.: Machine learning for high speed corner detection. In: 9th European Conference on Computer Vision (May 2006)