Tracking Random Sets of Vehicles in Terrain

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn 9, Issue , 2003, Pages 98-

  Sidenbladh, Hedvig ^a   Wirkander, Sven Lennart ^a  

^a SWEDISH DEFENCE RESEARCH AGENCY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; LANDFORMS; MONTE CARLO METHODS; RANDOM VARIABLES; VEHICLES;

A-PARTICLES; BAYESIAN THEORY; CARDINALITIES; FINITESET STATISTICS (FISST); NUMBER OF VEHICLES; PARTICLE FILTER; RANDOM SET;

SET THEORY;

EID: 84954419521     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2003.10097     Document Type: Conference Paper

References (24)

1. D. J. Ballantyne, H. Y. Chan, and M. A. Kouritzin. A branching particle-based nonlinear filter for multi-target tracking. In International Conference on Information Fusion, volume 1, pages WeA2:3-10, 2001.
2. Y. Bar-Shalom and E. Tse. Tracking in a cluttered environment with probabilistic data association. Automatica, 11:451-460, 1975.
3. S. Challa, B-N. Vo, and X. Wang. Bayesian approaches to track existence - IPDA and random sets. In International Conference on Information Fusion, volume 2, pages 1228-1235, 2002.
4. A. Doucet, N. de Freitas, and N. Gordon, editors. Sequential Monte Carlo Methods in Practice. Springer Verlag, New York, NY, USA, 2001.
5. A. Doucet, B-N. Vo, C. Andrieu, and M. Davy. Particle filtering for multi-target tracking and sensor management. In International Conference on Information Fusion, volume 1, pages 474-481, 2002.
6. T. E. Fortmann, Y. Bar-Shalom, and M. Scheffe. Sonar tracking of multiple targets using joint probabilistic data association. IEEE Journal of Oceanic Engineering, OE-8(3):173-184, 1983.
7. I. R. Goodman, R. P. S. Mahler, and H. T. Nguyen. Mathematics of Data Fusion. Kluwer Academic Publishers, Dordrecht, Netherlands, 1997.
8. N. Gordon, D. Salmond, and A. Smith. A novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings on Radar, Sonar and Navigation, 140(2):107-113, 1993.
9. P. J. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4):711-732, 1995.
10. C. Hue, J-P. Le Cadre, and P. Pérez. Sequential Monte Carlo methods for multiple target tracking and data fusion. IEEE Transactions on Signal Processing, 50(2):309-325, 2002.
11. M. Isard and A. Blake. Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision, 29(1):5-28, 1998.
12. M. Isard and J. MacCormick. BraMBLe: A Bayesian multiple-blob tracker. In IEEE International Conference on Computer Vision, ICCV, volume 2, pages 34-41, 2001.
13. K. Kastella, C. Kreucher, and M. A. Pagels. Nonlinear filtering for ground target applications. In SPIE Conference on Signal and Data Processing of Small Targets, volume 4048, pages 266-276, 2000.
14. C-C. Ke, J. G. Herrero, and J. Llinas. Comparative analysis of alternative ground target tracking techniques. In International Conference on Information Fusion, volume 2, pages WeB5:3-10, 2000.
15. R. Mahler. An Introduction to Multisource-Multitarget Statistics and its Applications. Lockheed Martin Technical Monograph, 2000.
16. R. Mahler and T. Zajic. Multitarget filtering using a multitarget first-order moment statistic. In SPIE Conference on Signal Processing, Sensor Fusion and Target Recognition, volume 4380, pages 184-195, 2001.
17. E. Mazor, A. Averbuch, Y. Bar-Shalom, and J. Dayan. IMM methods in target tracking: A survey. IEEE Transactions on Aerospace and Electronic Systems, 34(1):103-123, 1998.
18. J. Miller, A. Srivastava, and U. Grenander. Conditional-mean estimation via jump-diffusion processes in multiple target tracking/recognition. IEEE Transactions on Signal Processing, 43(11):2678-2690, 1995.
19. S. Musick, K. Kastella, and R. Mahler. A practical implementation of joint multitarget probabilities. In SPIE Conference on Signal Processing, Sensor Fusion and Target Recognition, volume 3374, pages 26-37, 1998.
20. D. Mušicki and R. Evans. Joint integrated probabilistic data association - JIPDA. In International Conference on Information Fusion, volume 2, pages 1120-1125, 2002.
21. D. Mušicki, R. Evans, and S. Stanković. Integrated probabilistic data association (IPDA). IEEE Transactions on Automatic Control, AC-39(6):1237-1241, 1994.
22. H. Sidenbladh. Multi-target particle filtering for the probability hypothesis density. In International Conference on Information Fusion, 2003.
23. E. P. Sodtke and J. Llinas. Terrain based tracking using position sensors. In International Conference on Information Fusion, volume 2, pages ThB1:27-32, 2001.
24. L. D. Stone. A Bayesian approach to multiple-target tracking. In D. L. Hall and J. Llinas, editors, Handbook of Multisensor Data Fusion, 2002.