Advertisement

Attenuated Sequential Importance Resampling (A-SIR) Algorithm for Object Tracking

  • Md. Zahidul Islam
  • Chi-Min Oh
  • Chil-Woo Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

This paper presents a newly developed attenuating resampling algorithm for particle filtering that can be applied to object tracking. In any filtering algorithm adopting concept of particles, especially in visual tracking, re-sampling is a vital process that determines the algorithm’s performance and accuracy in the implementation step.It is usually a linear function of the weight of the particles, which decide the number of particles copied. If we use many particles to prevent sample impoverishment, however, the system becomes computationally too expensive. For better real-time performance with high accuracy, we introduce a steep Attenuated Sequential Importance Re-sample (A-SIR) algorithm that can require fewer highly weighted particles by introducing a nonlinear function into the resampling method. Using our proposed algorithm, we have obtained very impressive results for visual tracking with only a few particles instead of many. Dynamic parameter setting increases the steepness of resampling and reduces computational time without degrading performance. Since resampling is not dependent on any particular application, the A-SIR analysis is appropriate for any type of particle filtering algorithm that adopts a resampling procedure. We show that the A-SIR algorithm can improve the performance of a complex visual tracking algorithm using only a few particles compared with a traditional SIR-based particle filter.

Keywords

Root Mean Square Error Object Tracking Importance Weight Visual Tracking Proposal Distribution 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sanjeev, A.M., Simon, M., Neil, G., Tim, C.: A tutorial on particle filters for online nonlinear/non-gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)CrossRefGoogle Scholar
  2. 2.
    Douc, R., Cappe, O.: Comparison of resampling schemes for particle filtering. In: 4th International Symposium on in Image and Signal Processing and Analysis, pp. 64–69 (2005)Google Scholar
  3. 3.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)Google Scholar
  4. 4.
    Yunqiang, C., Yong, R.: Real time object tracking in video sequences. In: Signals and Communications Technologies, Interactive Video, vol. II, pp. 67–88. Springer, Heidelberg (2006)Google Scholar
  5. 5.
    Artuar, L., Lyudmila, M., David, B.: Structural Similarity-based Object Tracking in Multimodality Surveillance Videos. Machine Vision and Applications 20(2), 71–83 (2009)CrossRefGoogle Scholar
  6. 6.
    Aherne, F.J., Thacker, N.A., Rockett, P.I.: The Bhattacharyya Metric as an Absolute Similarity Measure for Frequency Coded Data. Kybernetika 32(4), 1–7 (1997)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Liu, J.S., Chen, R.: Blind deconvolution via sequential imputation. Journal of American Statistical Association 90, 567–576 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Wu, G., Tang, Z.: A new resampling strategy about particle filter algorithm applied in Monte Carlo framework. In: Second International Conference on Intelligent Computation Technology and Automation, pp. 507–510 (2009)Google Scholar
  9. 9.
    Wang, F., Lin, Y.: Improving Particle Filter with A New Sampling Strategy. In: 4th International Conference on Computer Science and Education, pp. 408–412 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Md. Zahidul Islam
    • 1
  • Chi-Min Oh
    • 1
  • Chil-Woo Lee
    • 1
  1. 1.Chonnam National UniversityGwangjuSouth Korea

Personalised recommendations