Advertisement

2D Human Tracking by Efficient Model Fitting Using a Path Relinking Particle Filter

  • Juan José Pantrigo
  • Ángel Sánchez
  • Kostas Gianikellis
  • Antonio S. Montemayor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3179)

Abstract

This paper presents a 2D model-based Path Relinking Particle Filter (PRPF) algorithm for human motion tracking and analysis applications. PRPF algorithm hybridizes both Particle Filter and Path Relinking frameworks. The proposed algorithm increases the performance of general Particle Filter by improving the quality of the estimate, by adapting computational load to problem constraints and by reducing the number of required evaluations of the weighting function. A 2D human body model was used to develop tracking with PRPF. This model consists of a simple hierarchical set of articulated limbs, which is described by geometrical parameters. It is easily adaptable to the tracking application requirements. We have applied the PRPF algorithm to 2D human pose estimation in different movement tracking activities such as walking and jumping. Qualitative experimental results show that the model-based PRPF is appropriate in 2D human pose estimation studies.

Keywords

Particle Filter Path Relinking Sampling Importance Resampling Particle Filter Algorithm Pose Estimation Problem 
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.
    Wang, L., Weiming, H., Tieniu, T.: Recent developments in human motion analysis. Pattern Recognition 36, 585–601 (2003)CrossRefGoogle Scholar
  2. 2.
    Moeslund, B., Granum, E.: A Survey on Computer Vision-Based Human Motion Capture. Computer Vision and Image Understanding 81, 231–268 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Gavrila, D.: The visual analysis of human movement: a review. CVIU 73 1 (1999)Google Scholar
  4. 4.
    Kakadiaris, I., Sharma, R.: Editorial Introduction to the special issue on human modelling, analysis and syntesis. Machine Vision and Applications 14, 197–198 (2003)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Rohr, K.: Human movement analysis based on explicit motion models. In: Motion-based Recognition. ch. 8, pp. 171–198. Kluwer Academic Publishers, Dordrecht (1997)Google Scholar
  7. 7.
    Ju, S., Black, M., Yaccob, Y.: Cardboard people: a parameterized model of articulated image motion. In: IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 38–44 (1996)Google Scholar
  8. 8.
    Wachter, S., Nagel, H.-H.: Tracking persons in monocular image sequences. Computer Vision Image Understanding 74(3), 174–192 (1999)CrossRefGoogle Scholar
  9. 9.
    Leung, M.K., Yang, Y.H.: First sight: a human body outline labeling system. IEEE Trans. Pattern Anal. Mach. Intell. 17(4), 359–377 (1995)CrossRefGoogle Scholar
  10. 10.
    Zotkin, D., Duraiswami, R., Davis, L.: Joint Audio-Visual Tracking Using Particle Filters. EURASIP Journal on Applied Signal Processing 11, 1154–1164 (2002)Google Scholar
  11. 11.
    Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 126–133 (2000)Google Scholar
  12. 12.
    MacCormick, J., Blake, A.: Partitioned sampling, articulated objects and interfacequality hand tracking. In: Proc European Conf. Computer Vision (2000)Google Scholar
  13. 13.
    Pantrigo, J.J., Sánchez, A., Gianikellis, K., Duarte, A.: Path Relinking Particle Filter for Human Body Pose Estimation. Accepted paper to appear in LNCS (2004)Google Scholar
  14. 14.
    Glover, F., Laguna, M., Martí, R.: Scatter Search and Path Relinking: Foundations and Advances Designs. To appear in New Optimization techniques in Engineering (2003)Google Scholar
  15. 15.
    Glover, F.: A Template for Scatter Search and Path Relinking. In: Hao, J.-K., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds.) AE 1997. LNCS, vol. 1363, pp. 1–53. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  16. 16.
    Arulampalam, M., et al.: A Tutorial on Particle Filter for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Trans. On Signal Processing 50(2), 174–188 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Juan José Pantrigo
    • 1
  • Ángel Sánchez
    • 1
  • Kostas Gianikellis
    • 2
  • Antonio S. Montemayor
    • 1
  1. 1.Universidad Rey Juan CarlosMóstolesSpain
  2. 2.Universidad de ExtremaduraCáceresSpain

Personalised recommendations