Guided Importance Sampling Based Particle Filtering for Visual Tracking

  • Kazuhiko Kawamoto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


Linear estimation based sequential importance sampling methods for particle filters are proposed that can be used to model the rapid change of object motion in a video sequence. First a linear least–squares estimation is used to build a proposal function from observations, and then it is extended to a robust linear estimation. These sampling methods give a framework for tracking objects whose motion cannot be well modeled by a prior model. Finally a switching algorithm between the proposed method and the prior model based sampling method is proposed to achieve a filtering of both smooth and rapid evolution of the state. The ability of the proposed method is illustrated on a real video sequence involving a rapidly moving object.


Prior Distribution Feature Point Importance Sampling Prior Model Visual Tracking 
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  1. 1.
    Koller, D., Daniilidis, K., Nagel, H.-H.: Model-based object tracking in monocular image sequences of road traffic scenes. Int. J. Computer Vision 10, 257–281 (1993)CrossRefGoogle Scholar
  2. 2.
    Matthies, L., Szelinski, R., Kanade, T.: Kalman Filter-based Algorithms for Estimating Depth from Image Sequences. Int. J. Computer Vision 3(3), 209–238 (1989)CrossRefGoogle Scholar
  3. 3.
    Gennery, D.B.: Visual tracking of known three-dimensional objects. Int. J. Computer Vision 7(3), 243–270 (1992)CrossRefGoogle Scholar
  4. 4.
    Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc.–F 140(2), 107–113 (1993)Google Scholar
  5. 5.
    Kitagawa, G.: Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J. Comput. Graph. Stat. 5(1), 1–25 (1996)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Liu, J.S., Chen, R.: Sequential Monte Carlo methods for dynamical systems. J. of the American Statistical Association 93(443), 1032–1044 (1998)MATHCrossRefGoogle Scholar
  7. 7.
    Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10, 197–208 (2000)CrossRefGoogle Scholar
  8. 8.
    Isard, M., Blake, A.: Condensation – Conditional density propagation for visual tracking. Int. J. Computer Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  9. 9.
    Ichimura, N., Ikoma, N.: Filtering and Smoothing for Motion Trajectory of Feature Point Using Non-Gaussian State Space Model. IEICE Trans. Inf. & Syst. E84-D(6), 755–759 (2001)Google Scholar
  10. 10.
    Nummiaro, K., Koller-Meier, E., Gool, L.V.: An adaptive color-based particle filter. Image and Vision Computing 21, 99–110 (2003)CrossRefGoogle Scholar
  11. 11.
    Isard, M., Blake, A.: ICondensation: Unifying low-level and high-level tracking in a stochastic framework. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 893–908. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  12. 12.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. 4th Alvey Vision Conf, August 1988, pp. 147–151 (1988)Google Scholar
  13. 13.
    Shi, J., Tomasi, C.: Good Features to Track. In: Proc. CVPR, pp. 593–600 (1994)Google Scholar
  14. 14.
    Bouguet, J.Y.: Pyramidal Implementation of the Lucas Kanade Feature Tracker, Intel Corporation, Microprocessor Research Labs (2000)Google Scholar
  15. 15.
    Fischer, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kazuhiko Kawamoto
    • 1
  1. 1.Kyushu Institute of TechnologyKitakyushuJapan

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