Robust Particle Filtering for Object Tracking

  • Daniel Rowe
  • Ignasi Rius
  • Jordi Gonzàlez
  • Juan J. Villanueva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

This paper addresses the filtering problem when no assumption about linearity or gaussianity is made on the involved density functions. This approach, widely known as particle filtering, has been explored by several previous algorithms, including Condensation. Although it represented a new paradigm and promising results have been achieved, it has several unpleasant behaviours. We highlight these misbehaviours and propose an algorithm which deals with them. A test-bed, which allows proof-testing of new approaches, has been developed. The proposal has been successfully tested using both synthetic and real sequences.

Keywords

White Additive Gaussian Noise Object Tracking Background Clutter Distribution Run1 Time Sample Likelihood 
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.
    Arulampalam, S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for on-line non-linear/non-gaussian bayesian tracking. Tran. on Signal Processing 50(2), 174–188 (2002)CrossRefGoogle Scholar
  2. 2.
    Doucet, A.: On sequential simulation-based methods for bayesian filtering. Technical Report TR310, Cambridge University (1998)Google Scholar
  3. 3.
    Isard, M., Blake, A.: Condensation – conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  4. 4.
    King, O., Forsyth, D.A.: How does CONDENSATION behave with a finite number of samples? In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 695–709. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Russell, R., Norvig, P.: Artificial Intelligence, a Modern Approach, 2nd edn., ch. 13–15. Prentice Hall, Englewood Cliffs (2003)Google Scholar
  6. 6.
    van der Merwe, R., de Freitas, N., Doucet, A., Wan, E.: The Unscented Particle Filter. Technical Report TR380, Cambridge University (2000)Google Scholar
  7. 7.
    Varona, X., Gonzàlez, J., Roca, X., Villanueva, J.J.: iTrack: Image-based Probabilistic Tracking of People. In: 15th ICPR, Barcelona, Spain, vol. 3, pp. 1110–1113 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Daniel Rowe
    • 1
  • Ignasi Rius
    • 1
  • Jordi Gonzàlez
    • 1
  • Juan J. Villanueva
    • 1
  1. 1.Computer Vision Centre/Department of Computer ScienceUniversitat Autònoma de BarcelonaBellaterra, BarcelonaSpain

Personalised recommendations