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Using Optical Flow for Tracking

  • M. Lucena
  • J. M. Fuertes
  • N. Perez de la Blanca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

We present two observation models based on optical flow information to track objects using particle filter algorithms. Although, in principle, the optical flow information enables us to know the displacement of the objects present in a scene, it cannot be used directly to displace a model since flow estimation techniques lack the necessary precision. We will define instead two observation models for using into probabilistic tracking algorithms: the first uses an optical flow estimation computed previously, and the second is based directly on correlation techniques over two consecutive frames.

Keywords

Computer Vision Optical Flow Consecutive Frame Observation Model Uniform Background 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. Transactions of the ASME–Journal of Basic Engineering 82(Series D), 35–45 (1960)Google Scholar
  2. 2.
    Deutscher, J., Blake, A., North, B., Bascle, B.: Tracking through singularities and discontinuities by random sampling. In: Proceedings of International Conference on Computer Vision, vol. 2, pp. 1144–1149 (1999)Google Scholar
  3. 3.
    Sullivan, J., Blake, A., Isard, M., MacCormick, J.: Bayesian object localisation in images. International Journal of Computer Vision 44(2), 111–135 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17(1-3), 185–203 (1981)CrossRefGoogle Scholar
  5. 5.
    Adelson, E.H., Bergen, J.R.: The extraction of spatiotemporal energy in human and machine vision. In: Proceedings of IEEE Workshop on Visual Motion, pp. 151–156. IEEE Computer Society Press, Los Alamitos (1986)Google Scholar
  6. 6.
    McCane, B., Novins, K., Crannitch, D., Galvin, B.: On benchmarking optical flow. Computer Vision and Image Understanding 84, 126–143 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Proesmans, M., Van Gool, L., Pauwels, E., Oosterlinck, A.: Determination of optical flow and its discontinuities using non-linear diffusion. In: Proceedings of 3rd European Conference on Computer Vision, vol. 2, pp. 295–304 (1994)Google Scholar
  8. 8.
    Blake, A., Isard, M.: Active Contours. Springer, Heidelberg (1998)Google Scholar
  9. 9.
    Gelfand, A., Smith, A.: Sampling-based approaches to computing marginal densities. Journal of the American Statistical Association 85(410), 398–409 (1990)zbMATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: Proceedings of European Conference on Computer Vision, Cambridge, UK, pp. 343–356 (1996)Google Scholar
  11. 11.
    Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2000)Google Scholar
  12. 12.
    Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 43–77 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • M. Lucena
    • 1
  • J. M. Fuertes
    • 1
  • N. Perez de la Blanca
    • 2
  1. 1.Departamento de Informatica, Escuela Politecnica SuperiorUniversidad de JaenJaenSpain
  2. 2.Departamento de Ciencias de la Computacion e Inteligencia ArtificialETSII. Universidad de GranadaGranadaSpain

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