Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model

IEEE International Conference on Intelligent Robots and Systems

Volumn 1, Issue , 2003, Pages 254-259

  Khan, Zia ^a   Balch, Tucker ^a   Dellaert, Frank ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AUTOMATIC TARGET RECOGNITION; COLLISION AVOIDANCE; COMPUTATIONAL COMPLEXITY; COMPUTER VISION; MARKOV PROCESSES; MATHEMATICAL MODELS; MOTION CONTROL; PROBABILISTIC LOGICS; WAVE FILTERS;

EXPONENTIAL COMPLEXITY; PARTICLE FILTERS;

TRACKING (POSITION);

EID: 0347410634     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. D. Reid, "An algorithm for tracking multiple targets," IEEE Trans. on Automation and Control, vol. AC-24, pp. 84-90, December 1979.
2. Y. Bar-Shalom, T. Fortmann, and M. Scheffe, "Joint probabilistic data association for multiple targets in clutter," in Proc. Conf. on Information Sciences and Systems, 1980.
3. T. Fortmann, Y. Bar-Shalom, and M. Scheffe, "Sonar tracking of multiple targets using joint probabilistic data association," IEEE Journal of Oceanic Engineering, vol. 8, July 1983.
4. R. Deriche and O. Faugeras, "Tracking line segments," Image and Vision Computing, vol. 8, pp. 261-270, 1990.
5. I. Cox and J. Leonard, "Modeling a dynamic environment using a Bayesian multiple hypothesis approach," Artificial Intelligence, vol. 66, pp. 311-344, April 1994.
6. C. Rasmussen and G. Hager, "Probabilistic data association methods for tracking complex visual objects," PAMI, vol. 23, pp. 560-576, June 2001.
7. D. Schulz, W. Burgard, D. Fox, and A. B. Cremers., "Tracking multiple moving targets with a mobile robot using particle filters and statistical data association," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2001.
8. T. Balch, Z. Khan, and M. Veloso, "Automatically tracking and analyzing the behavior of live insect colonies," in Proc. Autonomous Agents 2001, (Montreal), 2001.
9. N. Gordon, D. Salmond, and A. Smith, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation," IEE Procedings F, vol. 140, no. 2, pp. 107-113, 1993.
10. M. Isard and A. Blake, "Contour tracking by stochastic propagation of conditional density," in Eur. Conf. on Computer Vision (ECCV), pp. 343-356, 1996.
11. J. Carpenter, P. Clifford, and P. Fernhead, "An improved particle filter for non-linear problems," tech. rep., Department of Statistics, University of Oxford, 1997.
12. F. Dellaert, D. Fox, W. Burgard, and S. Thrun, "Monte Carlo Localization for mobile robots," in IEEE Int. Conf. on Robotics and Automation (ICRA), 1999.
13. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking," IEEE Transactions on Signal Processing, vol. 50, pp. 174-188, Feb. 2002.
14. N. Jojic and B. Frey, "Learning flexible sprites in video layers," in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2001.
15. G. Winkler, Image analysis, random fields and dynamic Monte Carlo methods. Springer Verlag, 1995.
16. S. Li, Markov Random Field Modeling in Computer Vision. Springer, 1995.