Swarm Intelligence Based Searching Schemes for Articulated 3D Body Motion Tracking

  • Bogdan Kwolek
  • Tomasz Krzeszowski
  • Konrad Wojciechowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6915)

Abstract

We investigate swarm intelligence based searching schemes for effective articulated human body tracking. The fitness function is smoothed in an annealing scheme and then quantized. This allows us to extract a pool of candidate best particles. The algorithm selects a global best from such a pool. We propose a global-local annealed particle swarm optimization to alleviate the inconsistencies between the observed human pose and the estimated configuration of the 3D model. At the beginning of each optimization cycle, estimation of the pose of the whole body takes place and then the limb poses are refined locally using smaller number of particles. The investigated searching schemes were compared by analyses carried out both through qualitative visual evaluations as well as quantitatively through the use of the motion capture data as ground truth. The experimental results show that our algorithm outperforms the other swarm intelligence searching schemes. The images were captured using multi-camera system consisting of calibrated and synchronized cameras.

Keywords

Particle Swarm Optimization Particle Swarm Optimization Algorithm Motion Capture Swarm Intelligence Motion Capture Data 
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.
    Arsic, D., Lyutskanov, A., Rigoll, G., Kwolek, B.: Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework. In: IEEE Int. Workshop on Performance Evaluation of Tracking and Surveillance, pp. 30–37. IEEE Press, Piscataway (2009)Google Scholar
  2. 2.
    Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. on Evolutionary Computation 6(1), 58–73 (2002)CrossRefGoogle Scholar
  3. 3.
    Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: IEEE Int. Conf. on Pattern Recognition, pp. 126–133 (2000)Google Scholar
  4. 4.
    Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. Int. J. Comput. Vision 61(2), 185–205 (2005)CrossRefGoogle Scholar
  5. 5.
    Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for bayesian filtering. Statistics and Computing 10(1), 197–208 (2000)CrossRefGoogle Scholar
  6. 6.
    Gavrila, D.M., Davis, L.S.: 3-D model-based tracking of humans in action: a multi-view approach. In: Proc. of the Conf. on Computer Vision and Pattern Recognition (CVPR 1996), pp. 73–80. IEEE Computer Society, Washington, DC (1996)CrossRefGoogle Scholar
  7. 7.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of IEEE Int. Conf. on Neural Networks, pp. 1942–1948. IEEE Press, Piscataway (1995)CrossRefGoogle Scholar
  8. 8.
    Krzeszowski, T., Kwolek, B., Wojciechowski, K.: GPU-accelerated tracking of the motion of 3D articulated figure. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6374, pp. 155–162. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Krzeszowski, T., Kwolek, B., Wojciechowski, K.: Model-based 3D human motion capture using global-local particle swarm optimizations. In: Burduk, R., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds.) Computer Recognition Systems 4. Advances in Intelligent and Soft Computing, vol. 95, pp. 297–306. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Lee, M.W., Cohen, I.: A model-based approach for estimating human 3D poses in static images. IEEE Trans. Pattern Anal. Mach. Intell. 28, 905–916 (2006)CrossRefGoogle Scholar
  11. 11.
    Schmidt, J., Fritsch, J., Kwolek, B.: Kernel particle filter for real-time 3D body tracking in monocular color images. In: IEEE Int. Conf. on Face and Gesture Rec., pp. 567–572. IEEE Computer Society Press, Southampton (2006)Google Scholar
  12. 12.
    Sidenbladh, H., Black, M., Fleet, D.: Stochastic tracking of 3D human figures using 2D image motion. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 702–718. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    Sigal, L., Balan, A., Black, M.: HumanEva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. Int. Journal of Computer Vision 87, 4–27 (2010)CrossRefGoogle Scholar
  14. 14.
    Sminchisescu, C., Kanaujia, A., Li, Z., Metaxas, D.: Discriminative density propagation for 3D human motion estimation. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. I:390–I:397 (2005)Google Scholar
  15. 15.
    Zhang, X., Hu, W., Wang, X., Kong, Y., Xie, N., Wang, H., Ling, H., Maybank, S.: A swarm intelligence based searching strategy for articulated 3D human body tracking. In: IEEE Workshop on 3D Information Extraction for Video Analysis and Mining in Conjuction with CVPR, pp. 45–50. IEEE, Los Alamitos (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bogdan Kwolek
    • 1
  • Tomasz Krzeszowski
    • 1
  • Konrad Wojciechowski
    • 1
  1. 1.Polish-Japanese Institute of Information TechnologyWarszawaPoland

Personalised recommendations