Real-Time Tracking of Full-Body Motion Using Parallel Particle Swarm Optimization with a Pool of Best Particles

  • Tomasz Krzeszowski
  • Bogdan Kwolek
  • Boguslaw Rymut
  • Konrad Wojciechowski
  • Henryk Josinski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7269)

Abstract

In this paper we present a particle swarm optimization (PSO) based approach for marker-less full body motion tracking. The objective 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 to force the PSO jump out of stagnation. Experiments on 4-camera datasets demonstrate the robustness and accuracy of our method. The tracking is conducted on 2 PC nodes with multi-core CPUs, connected by 1 GigE. This makes our system capable of accurately recovering full body movements with 14 fps.

Keywords

Particle Swarm Optimization Particle Swarm Optimization Algorithm Motion Capture Annealed Particle Annealing Scheme 
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.

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References

  1. 1.
    Chapman, B., Jost, G., van der Pas, R., Kuck, D.: Using OpenMP: Portable Shared Memory Parallel Programming. The MIT Press (2007)Google Scholar
  2. 2.
    Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Tr. Evolut. Comp. 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.
    John, V., Trucco, E., Ivekovic, S.: Markerless human articulated tracking using hierarchical particle swarm optimisation. Image Vis. Comput. 28, 1530–1547 (2010)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.
    Kwolek, B., Krzeszowski, T., Wojciechowski, K.: Swarm Intelligence Based Searching Schemes for Articulated 3D Body Motion Tracking. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2011. LNCS, vol. 6915, pp. 115–126. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    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 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tomasz Krzeszowski
    • 2
    • 1
  • Bogdan Kwolek
    • 2
    • 1
  • Boguslaw Rymut
    • 2
    • 1
  • Konrad Wojciechowski
    • 1
  • Henryk Josinski
    • 1
  1. 1.Polish-Japanese Institute of Information TechnologyWarszawaPoland
  2. 2.Rzeszów University of TechnologyRzeszówPoland

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