Real time viterbi optimization of hidden Markov Models for multi target tracking

2007 IEEE Workshop on Motion and Video Computing, WMVC 2007

Volumn , Issue , 2007, Pages 2-

  Ardö, Håkan ^a   Åström, Kalle ^a   Berthilsson, Rikard ^a  

^a LUND UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; DYNAMIC PROGRAMMING; HIDDEN MARKOV MODELS; OPTIMIZATION; REAL TIME SYSTEMS; VITERBI ALGORITHM;

FINITE DELAYS; OPTIMUM STATE SEQUENCES; STATE SEQUENCES; VITERBI OPTIMIZATION;

TARGET TRACKING;

EID: 34547234046     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WMVC.2007.33     Document Type: Conference Paper

References (21)

1. Håkan Ardö and Rikard Berthilsson. Adaptive background estimation using intensity independent features. Proc. British Machine Vision Conference, 03, 2006.
2. Marcelo G.S. Bruno and Jose M.E Moura. Multiframe detector/tracker: Optimal performance. IEEE Transactions on Aerospace and Electronic Systems, 37(3):925-946, 2001.
3. M.G.S. Bruno. Sequential importance sampling filtering for target tracking in image sequences. IEEE Signal Processing Letters, 10(8):246-249, 2003.
4. R. S. Bucy and K. D. Senne. Digital synthesis of non-linear filters. Automatica, 7:287-298, 1971.
5. S. J. Davey, D. A. Gray, and S. B. Colegrove. A markov model for initiating tracks with the probabilistic multihypothesis tracker. Information Fusion, 2002. Proceedings of the Fifth International Conference on, 1:735-742 vol.1, 2002.
6. Mei Han, Wei Xu, Hai Tao, and Yihong Gong. An algorithm for multiple object trajectory tracking, cvpr, 01:864-871, 2004.
7. David Hogg. Model-based vision: a program to see a walking person. Image and vision computing, 1(1):5-20, 1983.
8. C. Hue, J.-P. Le Cadre, and P. Perez. Tracking multiple objects with particle filtering. Aerospace and Electronic Systems, IEEE Transactions on, 38(3):791-812, 2002.
9. M. Isard and A. Blake. A. visual tracking by stochastic propagation of conditional density. In 4th European Conf. Computer Vision, 1996.
10. Michael Isard and Andrew Blake. !CONDENSATION: Unifying low-level and high-level tracking in a stochastic framework. Lecture Notes in Computer Science, 1406:893-908, 1998.
11. Michael Isard and John MacCormick. Bramble: A bayesian multiple-blob tracker. In ICCV, pages 34-41, 2001.
12. R. E. Kalman. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82-D:35-45, 1960.
13. Jien Kato, T. Watanabe, S. Joga, Ying Liu, and H. Hase. An hmm/mrf-based stochastic framework for robust vehicle tracking. IEEE Transactions on Intelligent Transportation Systems, 5(3): 142-154, 2004.
14. Javier Movellan, John Hershey, and Josh Susskind. Real-time video tracking using convolution hmms. In CVPR, 2004.
15. Gordon N.J., Salmond D.J., and Smith A.F.M. Novel approach to nonlinear/non-gaussian bayesian state estimation. Radar and Signal Processing, IEE Proceedings F, 140(2): 107-113, 1993.
16. Maurizio Pilu. Video stabilization as a variational problem and numerical solution with the viterbi method, cvpr, 01:625-630, 2004.
17. L.R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE, 77(2):257-286, 1989.
18. D.B. Reid. An algorithm for tracking multiple targets. IEEE Transaction on Automatic Control, 24(6): 843-854, 1979.
19. R. W. Sittler. An optimal data association problem in surveillance theory. IEEE Transactions on Aerospace and Electronic Systems, MIL-8(Apr): 125-139, 1964.
20. R. L. Streit and T. E. Luginbuhl. Probabilistic multihypothesis tracking. Technical Report 10428, NUWC, Newport, RI, 1995.
21. T. Fortmann Y. Bar-Shalom and M. Scheffe. Joint probabilistic data association for multiple targets in clutter. In Conf. on Information Sciences and Systems, 1980.