Weighted Bayesian Network for visual tracking

Proceedings - International Conference on Pattern Recognition

Volumn 1, Issue , 2006, Pages 523-526

  Zhou, Yue ^a   Huang, Thomas S ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL PROBABILITIES; VISUAL TRACKING; WEIGHTED BAYESIAN NETWORK;

COMPUTER VISION; GRAPHIC METHODS; MATHEMATICAL MODELS; PROBABILITY; TRACKING (POSITION);

BAYESIAN NETWORKS;

EID: 34047218909     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2006.1188     Document Type: Conference Paper

References (16)

1. Y. Chen, Y. Rui, and T. S. Huang. Jpdaf based hmm or real-time contour tracking. pages 543-550, 2001.
2. D. Comaniciu, V. Ramesh, and P. Meer. Kernel-based object tracking. IEEE Trans. Pattern Anal. Much. Intell., 25(5):564-575, 2003.
3. R. G. Cowell, A. Dawid, S. L. Lauritzen, and D. J. Spiegel-halter. Probabilistic Networks and Expert Systems. 1999.
4. I. J. Cox and S. L. Hingorani, An efficient implementation of reid's multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Trans. Pattern Anal. Mach. Intell., 18(2):138-150, 1996.
5. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society series B, 39:1-38, 1977.
6. M. Isard and A. Blake. Condensationlconditional density propagation forvisual tracking. Int. J. Comput. Vision, 29(1):5-28, 1998.
7. M. I. Jordan, editor, Learning in graphical models. MIT Press, Cambridge, MA, USA, 1999.
8. E. Kalman, Rudolph. A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering, 82(Series D):35-45, 1960,
9. D. G. Lowe. Distinctive image features from scale-invariant key-points. Int. J. Comput Vision, 60(2):91-110, 2004.
10. J. MacCormick and A. Blake. A probabilistic exclusion principle for tracking multiple objects. Int. J. Comput. Vision, 39(1):57-71, 2000.
11. R. M. Neal. Probabilistic inference using markov chain monte carlo methods. Technical report, Univ. of Toronto, 1993.
12. L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition, pages 267-296, 1990.
13. D. Reid. An algorithm for tracking multiple targets. Automatic Control, IEEE Transactions on., 24(6):843-854, 1979.
14. V. N. Vapnik. The nature of statistical learning theory. Springer-Verlag New York, Inc., New York, NY, USA, 1995.
15. Y. Wu and T. S. Huang. Robust visual tracking by integrating multiple cues based on co-inference learning. Int. J. Comput. Vision, 58(1):55-71, 2004.
16. J. S. Yedidia, W. T. Freeman, and Y. Weiss. Understanding belief propagation and its generalizations. pages 239-269, 2003.