The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimo dal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.
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Anderson and Moore 1979. Optimal Filtering. Prentice Hall.
Astrom, K.J. 1970. Introduction to Stochastic Control Theory. Academic Press.
Bar-Shalom, Y. and Fortmann, T. 1988. Tracking and Data Association. Academic Press.
Bartels, R., Beatty, J., and Barsky, B. 1987. An Introduction to Splines for use in Computer Graphics and Geometric Modeling. Morgan Kaufmann.
Baumberg, A. and Hogg, D. 1994. Learning flexible models from image sequences. In Proc. 3rd European Conference on Computer Vision, J.-O. Eklundh (Ed.), Springer-Verlag, pp. 299-308.
Baumberg, A. and Hogg, D. 1995. Generating spatiotemporal models from examples. In Proc. of the British Machine Vision Conference, vol. 2, pp. 413-422.
Blake, A., Curwen, R., and Zisserman, A. 1993. A framework for spatio-temporal control in the tracking of visual contours. Int. Journal of Computer Vision, 11(2):127-145.
Blake, A. and Isard, M. 1994. 3D position, attitude and shape input using video tracking of hands and lips. In Proc. Siggraph, ACM, pp. 185-192.
Blake, A., Isard, M., and Reynard, D. 1995. Learning to track the visual motion of contours Artificial Intelligence, 78: 101-134.
Bucy, R. 1969. Bayes theorem and digital realizations for non-linear filters. J. Astronautical Sciences, 17(2):80-94.
Cipolla, R. and Blake, A. 1990. The dynamic analysis of apparent contours. In Proc. 3rd Int. Conf. on Computer Vision, pp. 616-625.
Cootes, T., Taylor, C., Lanitis, A., Cooper, D., and Graham, J. 1993. Building and using flexible models incorporating grey-level information. In Proc. 4th Int. Conf. on Computer Vision, pp. 242-246.
Dickmanns, E. and Graefe, V. 1988. Applications of dynamic monocular machine vision.MachineVision and Applications, 1:241-261.
Fischler, M. and Bolles, R. 1981. Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography. Commun. Assoc. Comp. Mach., 24:381-395.
Fischler, M.A. and Elschlager, R.A. 1973. The representation and matching of pictorial structures. IEEE. Trans. Computers, C-22:1.
Gelb, A. (Ed.) 1974. Applied Optimal Estimation. MIT Press: Cambridge, MA.
Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Analysis and Machine Intelligence, 6(6):721-741.
Gennery, D. 1992. Visual tracking of known three-dimensional objects. Int. Journal of Computer Vision, 7(3):243-270.
Goodwin, C. and Sin, K. 1984. Adaptive Filtering Prediction and Control. Prentice-Hall.
Gordon, N., Salmond, D., and Smith, A. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. F, 140(2):107-113.
Grenander, U., Chow, Y., and Keenan, D.M. 1991. HANDS. A Pattern Theoretical Study of Biological Shapes. Springer-Verlag: New York.
Hager, G. 1990. Sensor Fusion and Planning: A Computational Approach. Kluwer Academic Publishers.
Harris, C. 1992. Tracking with rigid models. In Active Vision, A. Blake and A. Yuille (Eds.), MIT Press: Cambridge, MA, pp. 59-74.
Hogg, D. 1983. Model-based vision: A program to see a walking person, Image and Vision Computing, 1(1):5-20.
Huttenlocher, D., Noh, J., and Rucklidge, W. 1993. Tracking nonrigid objects in complex scenes. In Proc. 4th Int. Conf. on Computer Vision, pp. 93-101.
Isard, M. and Blake, A. 1996. Visual tracking by stochastic propagation of conditional density. In Proc. 4th European Conf. on Computer Vision, Cambridge, England, pp. 343-356.
Kass, M., Witkin, A., and Terzopoulos, D. 1987. Snakes: Active contour models. In Proc. 1st Int. Conf. on Computer Vision, pp. 259-268.
Kitagawa, G. 1996. Monte-carlo filter and smoother for non-Gaussian nonlinear state space models. J. Computational and Graphical Statistics, 5(1):1-25.
Koenderink, J. and Van Doorn, A. 1991. Affine structure from motion, J. Optical Soc. of America A., 8(2):337-385.
Lowe, D. 1991. Fitting parameterised 3D models to images. IEEE Trans. Pattern Analysis and Machine Intelligence, 13(5):441-450.
Lowe, D. 1992. Robust model-based motion tracking through the integration of search and estimation. Int. Journal of Computer Vision, 8(2):113-122.
Matthies, L.H., Kanade, T., and Szeliski, R. 1989. Kalman filterbased algorithms for estimating depth from image sequences. Int. Journal of Computer Vision, 3:209-236.
Menet, S., Saint-Marc, P., and Medioni, G. 1990. B-snakes: Implementation and application to stereo. In Proceedings DARPA, pp. 720-726.
Miller, M., Srivasta, A., and Grenander, U. 1995. Conditional-mean estimation via jump-diffusion processes in multiple target tracking/ recognition. IEEE Transactions on Signal Processing, 43(11): 2678-2690.
Papoulis, A. 1990. Probability and Statistics. Prentice-Hall.
Press, W., Teukolsky, S., Vetterling, W., and Flannery, B. 1988. Numerical Recipes in C. Cambridge University Press.
Rabiner, L. and Bing-Hwang, J. 1993. Fundamentals of Speech Recognition. Prentice-Hall.
Rao, B. 1992. Data association methods for tracking systems. In Active Vision, A. Blake and A. Yuille (Eds.), MIT Press: Cambridge, MA, pp. 91-105.
Rao, C. 1973. Linear Statistical Inference and its Applications. John Wiley and Sons: New York.
Rehg, J. and Kanade, T. 1994. Visual tracking of high dof articulated structures: An application to human hand tracking. In Proc. 3rd European Conference on Computer Vision, J.-O. Eklundh (Ed.), Springer-Verlag, pp. 35-46.
Reynard, D., Wildenberg, A., Blake, A., and Marchant, J. 1996. Learning dynamics of complex motions from image sequences. In Proc. 4th European Conf. on Computer Vision, Cambridge, England, pp. 357-368.
Ripley, B. 1987. Stochastic Simulation. Wiley: New York.
Ripley, B. and Sutherland, A. 1990. Finding spiral structures in images of galaxies. Phil. Trans. R. Soc. Lond. A., 332, 1627:477-485.
Rowe, S. and Blake, A. 1996. Statistical feature modelling for active contours. In Proc. 4th European Conf. on Computer Vision, Cambridge, England, pp. 560-569.
Sorenson, H.W. and Alspach, D.L. 1971. Recursive Bayesian estimation using Gaussian sums. Automatica, 7:465-479.
Storvik, G. 1994. A Bayesian approach to dynamic contours through stochastic sampling and simulated annealing. IEEE Trans. Pattern Analysis and Machine Intelligence, 16(10):976-986.
Sullivan, G. 1992. Visual interpretation of known objects in constrained scenes. Phil. Trans. R. Soc. Lond. B., 337:361-370.
Terzopoulos, D. and Metaxas, D. 1991. Dynamic 3D models with local and global deformations: Deformable superquadrics.IEEE Trans. Pattern Analysis and Machine Intelligence, 13:7.
Terzopoulos, D. and Szeliski, R. 1992.Tracking with Kalman snakes. In Active Vision, A. Blake and A. Yuille (Eds.), MIT Press: Cambridge, MA, pp. 3-20.
Ullman, S. and Basri, R. 1991. Recognition by linear combinations of models. IEEE Trans. Pattern Analysis and Machine Intelligence, 13(10):992-1006.
Yuille, A. and Hallinan, P. 1992. Deformable templates. In Acive Vision, A. Blake and A. Yuille (Eds.), MIT Press: Cambridge, MA, pp. 20-38.
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Isard, M., Blake, A. CONDENSATION—Conditional Density Propagation for Visual Tracking. International Journal of Computer Vision 29, 5–28 (1998). https://doi.org/10.1023/A:1008078328650