A probabilistic background model for tracking

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1843, Issue , 2000, Pages 336-350

  Rittscher, J ^a   Kato, J ^a   Joga, S ^b   Blake, A ^c  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b TELECOM PARISTECH   (France)

^c MICROSOFT RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; HIDDEN MARKOV MODELS; IMPORTANCE SAMPLING; MONTE CARLO METHODS; STOCHASTIC SYSTEMS;

BACKGROUND MODEL; HIDDEN STATE; LEVEL PROCESS; MARKOV RANDOM FIELDS; MODEL FUNCTIONS; PARTICLE FILTER; STATISTICAL DEPENDENCE; STOCHASTIC FILTERS;

STOCHASTIC MODELS;

EID: 84944029623     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45053-x_22     Document Type: Conference Paper

References (21)

1. J. Besag. Spatial interaction and the statistical analysis of lattice systems. J. R. Statist. Soc. B, 36:192-236, 1974.
2. D. Beymer, P. McLauchlan, B. Coifman, and J. Malik. A real-time computer vision system for measuring traffic pramameters. In IEEE Conf. ComputerVision and Pattern Recognition, June 1997, Puerto Rico, 1997.
3. F. Divino, A. Frigessi, and P. J. Green. Penalized pseudolikelihood inference in spatial interaction models with covariates. Scandinavian Journal of Statistics (to appear), 1998.
4. N. Ferrier, S. Rowe, and A. Blake. Real-time traffic monitoring. In Proc. 2nd IEEEWorkshop on Applications of Computer Vision, pages 81-88, 1994.
5. S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. PAMI, 6:721-741, 1984.
6. I. Haritaoglu, D. Harwood, and L. S. Davis. W4-a real time system for detection and tracking people and their parts. In Proc. Conf. Face and Gesture Recognition, Nara, Japan, 1998.
7. M. Isard and A. Blake. Contour tracking by stochastic propagation of conditional density. In Proc. European Conf. on Computer Vision, Cambridge, UK, 1996.
8. M.A. Isard and A. Blake. ICondensation: Unifying low-level and high-level tracking in a stochastic framework. In Proc. 5th European Conf. Computer Vision, pages 893-908, 1998.
9. B.-H. Juang and L.R. Rabiner. Mixture autoregressive hidden markov models for speech signals. IEEE Trans. Acoustics, Speech, and Signal Processing, December:1404-1413, 1985.
10. D. Koller, J. Weber, and J. Malik. Robust multiple car tracking with occlusion reasoning. In Proc. of ECCV 94, Stockholm, Sweden, pages 189-196, 1994.
11. J. MacCormick and A. Blake. A probablilistic exclusion principle for tracking multiple objects. In Proc. 7th Int. Conf. on Computer Vision, volume 2, pages 572-578, 1999.
12. S.J. Maybank, A.D. Worrall, and G.D. Sullivan. Filter for car tracking based on acceleration and steering angle. In Proc. 7th BMVC, 1996.
13. N. Paragios and R. Deriche. A PDE-based Level Set Approach for detection and tracking of moving objects. Technical Report 3173, INRIA Sophia Antipolis, 1997.
14. L. R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE, 77(2):257-286, February 1989.
15. P. Remagnino, A. Baumberg, T. Grove, D. Hogg, T. Tan, A. Worrall, and K. Baker. An integratedtraffic and pedestrian model-based vision system. In Proc. of BMVC ’97, Univ. of Essex, volume 2, pages 380-398, 1997.
16. J. Rittscher and A. Blake. Classification of human body motion. In Proc. 7th Int. Conf. on Computer Vision, pages 634-639, 1999.
17. S.M. Rowe and A. Blake. Statistical background modelling for tracking with a virtual camera. In Proc. British Machine Vision Conf., volume 2, pages 423-432, 1995.
18. G.D Sullivan, K.D. Baker, and A.D. Worrall. Model-based vehicle detection and classification using orthographic approximations. In Proc. of 7th BMVC, pages 695-704, 1996.
19. J. Sullivan, A. Blake, M. Isard, and J. MacCormick. Object localisation by Baysian correlation. In Proc. 7th Int. Conf. on Computer Vision, volume 2, pages 1068-1075, 1999.
20. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. Wallflower: Principles and practice of background maintenance. In Proc. 7th Int. Conf. on Computer Vision, pages 255-261, 1999.
21. G. Winkler. Image analysis, random fields and dynamics Monte Carlo methods: a mathematical introduction. Spinger, 1995.