Integrating shape and dynamic probabilistic models for data association and tracking

Proceedings of the IEEE Conference on Decision and Control

Volumn 3, Issue , 2002, Pages 2409-2414

  Gennari, Giambattista ^a   Chiuso, Alessandro ^a   Cuzzolin, Fabio ^a   Frezza, Ruggero ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

AIR TRAFFIC CONTROL; COMPUTER SIMULATION; CONSTRAINT THEORY; DIGITAL FILTERS; MULTI AGENT SYSTEMS; PARAMETER ESTIMATION; PROBABILITY; ROBUSTNESS (CONTROL SYSTEMS);

DATA ASSOCIATION; DATA TRACKING; DYNAMIC PROBABILISTIC MODELS; INTEGRATING SHAPE; SHAPE CONSTRAINTS;

ALGORITHMS;

EID: 0036991373     PISSN: 01912216     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Y. Bar-Shalom and T. Fortman, Tracking and data association, Academic Press, 1988.
2. N. Bergman and A. Doucet, Markov chain monte carlo data association for target tracking, IEEE Int. Conference on Acoustics, Speech and Signal Processing, 2000.
3. S.J. Davey and S.B. Colgrove, A unified probabilistic data association filter with multiple models, Tech. Report DSTO-TR-1184, Surveillance System Division, Electronic and Surveillance Research Lab., 2001.
4. A.P. Dempster. Upper and lower probabilities induced by a multivariate mapping. Annals of Mathematical Statistics 38 (1967), 325-339.
5. ____, Upper and lower probability inferences based on a sample from a finite univariate population, Biometrika 54 (1967), 515-528.
6. ____, Upper and lower probabilities generated by a random closed interval, Annals of Mathematical Statistics 39 (1968), 957-966.
7. I. Dryden and K.V. Mardia, General shape distributions in a plane, Adv. Appl. Prob. 23 (1991), 259:276.
8. R. Karlsoon and F. Gustafsson, Monte carlo data association for multiple target tracking, IEEE Workshop on Target Tracking, 2001.
9. D.G. Kendall, A survey of the statistical theory of shape, Statistical Science 4(2) (1989), 87-120.
10. C. Rasmussen and G.D. Hager, Joint probabilistic techniques for tracking multi-part objects, Int. Conf. on Computer Vision and Pattern Recognition, 1998.
11. ____, Probabilistic data association methods for tracking complex visual objects, IEEE Transaction on Patter Analysis and Machine Intelligence 23 (2001), 560-576.
12. G. Schafer, A mathematical theory of evidence, Princeton University Press, 1976.