On Boosted and Adaptive Particle Filters for Affine-Invariant Target Tracking in Infrared Imagery

  • Guoliang Fan
  • Vijay Venkataraman
  • Li Tang
  • Joseph P. Havlicek
Part of the Advances in Pattern Recognition book series (ACVPR)


We generalize the usual white noise acceleration target model by introducing an affine transformation to model the target aspect. This transformation is parameterized by scalar variables that describe the target shear, scale, and rotation and obey a first-order Markov chain. Our primary interest is in achieving robust Monte Carlo estimation in a complex state space under low signal-to-noise ratios, where a low-entropy unimodal likelihood function contributes to the failure of the conventional particle-filtering algorithms. Motivated by recently developed particle filter consistency checks, we develop a new track quality indicator that monitors tracking performance, triggering one of two actions as needed to improve the quality of the track. The first action is a boosting step, by which a local detector is defined based on the most recent tracker output to induce additional high-quality boosting particles. The original idea of boosting is extended here by encouraging positive interaction between the detector and the tracker. The second action is an adaptation step in which the system model self-adjusts to enhance tracking performance. In the context of affine-invariant target tracking, we compare these two techniques with respect to their effectiveness in improving the particle quality. We present experimental results that show that both techniques can improve the tracking performance by balancing the focus and the diversity of the particle distribution and by improving the particle filter consistency.


Particle Filter Tracking Performance Target Tracking Observation Model Target Template 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Chapter's References

  1. 1.
    Gordon, N.J., Salmond, D.J., Smith, A.F.M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F (Radar, Signal Process.) 140(2) (1993) 107– 113Google Scholar
  2. 2.
    Doucet, A., Freitas, J.F.G., Gordon, N.J.: Sequential Monte Carlo Methods in Practice. Springer-Verlag, New York (2001)MATHGoogle Scholar
  3. 3.
    Heijden, F.V.D.: Consistency check for particle filters. IEEE Trans. Pattern Anal. Machine Intell. 28(1) (2006) 140–145CrossRefGoogle Scholar
  4. 4.
    Chang, C., Ansari, R., Khokhar, A.: Multiple object tracking with kernel particle filter. In: Proc. IEEE Int. Conf. Comput. Vision, Pattern Recog. (2005) 566–573Google Scholar
  5. 5.
    Han, B., Zhu, Y., Comaniciu, D., Davis, L.: Kernel-based Bayesian filtering for object tracking. In: Proc. IEEE Int. Conf. Comput. Vision Pattern Recog. (2005) 227–234Google Scholar
  6. 6.
    Deutscher, J., Blake, A., Reid, I.D.: Articulated body motion capture by annealed particle filtering. In: Proc. IEEE Int. Conf. Comput. Vision Pattern Recog. (2000) 126–133Google Scholar
  7. 7.
    Bruno, M.G.S.: Sequential importance sampling filtering for target tracking in image sequences. IEEE Signal Process. Lett. 10(8) (2003) 246–249CrossRefGoogle Scholar
  8. 8.
    Bruno, M.G.S., Moura, J.M.F.: Multiframe detection/tracking in clutter: Optimal performance. IEEE Trans. Aerosp. Electron. Syst. 37(3) (2001) 925–946CrossRefGoogle Scholar
  9. 9.
    Zhou, S., Chellappa, R., Mogghaddam, B.: Adaptive visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Trans. Image Process. 13(11) (2004) 1491–1505CrossRefGoogle Scholar
  10. 10.
    Okuma, K., Taleghani, A., de Freitas, N., Little, J.J., Lowe, D.G.: A boosted particle filter: Multitarget detection and tracking. In: Proc. 8th Eur. Conf. Comput. Vision LNCS 3021 (2004) 28–39Google Scholar
  11. 11.
    Rathi, Y., Vaswani, N., Tannenbaum, A., Yezzi, A.: Particle filtering for geometric active contours with application to tracking moving and deforming objects. In: Proc. IEEE Int. Conf. Comput. Vision Pattern Recog. (2005) 2–9Google Scholar
  12. 12.
    Bruno, M.G.S.: Bayesian methods for multiaspect target tracking in image sequences. IEEE Trans. Signal Process. 52(7) (2004) 1848–1861CrossRefGoogle Scholar
  13. 13.
    Wang, P., Rehg, J.M.: A modular approach to the analysis and evaluation of particle filters for figure tracking. In: Proc. IEEE Int. Conf. Comput. Vision Pattern Recog. (2006) 790–797Google Scholar
  14. 14.
    Moura, J.M.F., Balram, N.: Noncausal Gauss Markov random fields: Parameter structure and estimation. IEEE Trans. Inform. Theory 39(4) (1993) 1333–1355MATHCrossRefGoogle Scholar
  15. 15.
    Moura, J.M.F., Balram, N.: Recursive structure of noncousal Gauss Markov random fields. IEEE Trans. Inform. Theory 38(2) (1992) 334–354CrossRefGoogle Scholar
  16. 16.
    Foley, J., van Dam, A., Feiner, S., Hughes, J.: Computer Graphics: Principles and Practice. 2nd edn. Addison-Wesley, Boston (1990)Google Scholar
  17. 17.
    Lichtenauer, J., Reinders, M., Hendriks, E.: Influence of the observation likelihood function on particle filtering performance in tracking applications. In: 6th IEEE Int. Conf. Automatic Face Gesture Recog. (2004) 767–772Google Scholar
  18. 18.
    Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50(2) (2002) 174–188CrossRefGoogle Scholar
  19. 19.
    Pitt, M.K., Shephard, N.: Filtering via simulation: auxiliary particle filters. J. Am. Stat. Assoc. 94(446) (1999) 590–599MATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Huang, Y., Djuric, P.M.: A hybrid importance function for particle filtering. IEEE Signal Process. Lett. 11(3) (2004) 404–406CrossRefGoogle Scholar
  21. 21.
    Shen, C., Brooks, M.J., van den Hengel, A.: Augmented particle filtering for efficient visual tracking. In: Proc. IEEE Int. Conf. Image Process. 3 (2005) 856–859Google Scholar
  22. 22.
    Rui, Y., Chen, Y.: Better proposal distributions: Object tracking using unscented particle filter. In: Proc. IEEE Int. Conf. Comput. Vision, Pattern Recog. 2 (2001) 786–793Google Scholar
  23. 23.
    Wu, Y., Huang, T.S.: A co-inference approach to robust visual tracking. In: Proc. IEEE Int. Conf. Comput. Vision 2 (2001) 26–33Google Scholar
  24. 24.
    Chang, C., Ansari, R.: Kernel particle filter for visual tracking. IEEE Signal Process. Lett. 12(3) (2005) 242–245CrossRefGoogle Scholar
  25. 25.
    Philomin, V., Duraiswami, R., Davis, L.S.: Quasi-random sampling for condensation. In: Proc. Eur. Conf. Comput. Vision 2 (2000) 134–149Google Scholar
  26. 26.
    Maggio, E., Cavallaro, A.: Hybrid particle filter and mean shift tracker with adaptive transition model. In: Proc. IEEE Int. Conf. Acoust. Speech Signal Proc., Philadelphia, (2005)Google Scholar
  27. 27.
    Torma, P., Szepesvri, C.: Enhancing particle filters using local likelihood sampling. In: Proc. 8th Eur. Conf. Comput. Vision, LNCS 3021 (2004) 16–27Google Scholar
  28. 28.
    Torma, P., Szepesvari, C.: On using likelihood-adjusted proposals in particle filtering: Local importance sampling. In: Proc. 4th Int. Symp. Image, Signal Process. Anal. (2005) 58–63Google Scholar
  29. 29.
    Andrieu, C., Doucet, A., Tadic, V.: Particle methods for change detection, system identification and control. Proc. IEEE 92(3) (2004) 423–438CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Guoliang Fan
    • 1
  • Vijay Venkataraman
    • 1
  • Li Tang
    • 1
  • Joseph P. Havlicek
    • 1
  1. 1.Oklahoma State UniversityStillwater

Personalised recommendations