Real-Time Multi-view Human Motion Tracking Using 3D Model and Latency Tolerant Parallel Particle Swarm Optimization

  • Bogdan Kwolek
  • Tomasz Krzeszowski
  • Konrad Wojciechowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6930)


This paper demonstrates how latency tolerant parallel particle swarm optimization can be used to achieve real-time full-body motion tracking. The tracking is realized using multi-view images and articulated 3D model with a truncated cones-based representation of the body. Each CPU core computes fitness score for a single camera. On each node the algorithm uses the current temporary best fitness value without waiting for the global best one from cooperating sub-swarms. The algorithm runs at 10 Hz on eight PC nodes connected by 1 GigE.


Motion Capture Object Tracking Motion Tracking Motion Capture Data Master Thread 
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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  1. 1.
    Chapman, B., Jost, G., van der Pas, R., Kuck, D.J.: Using OpenMP: Portable Shared Memory Parallel Programming. The MIT Press, Cambridge (2007)Google Scholar
  2. 2.
    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
  3. 3.
    Gavrila, D.M., Davis, L.S.: 3-D model-based tracking of humans in action: a multi-view approach. In: Proc. of the Int. Conf. on Computer Vision and Pattern Rec., CVPR 1996, pp. 73–80. IEEE Computer Society, Washington, DC (1996)Google Scholar
  4. 4.
    Grard, P., Gagalowicz, A.: Human body tracking using a 3D generic model applied to golf swing analysis. In: Int. Conf. on Computer Vision / Computer Graphics Collaboration Techniques and Applications (2003)Google Scholar
  5. 5.
    Ivekovic, S., John, V., Trucco, E.: Markerless multi-view articulated pose estimation using adaptive hierarchical particle swarm optimisation. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010. LNCS, vol. 6024, pp. 241–250. Springer, Heidelberg (2010)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.
    Krzeszowski, T., Kwolek, B., Wojciechowski, K.: Model-based 3D human motion capture using global-local particle swarm optimizations. In: Int. Conf. on Computer Recognition Systems. AISC, vol. 95, pp. 297–306. Springer, Heidelberg (2011)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.
    Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104, 90–126 (2006)CrossRefGoogle Scholar
  10. 10.
    Muendermann, L., Corazza, S., Andriacchi, T.: The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications. Journal of Neuroengineering and Rehabilitation 3(1) (2006)Google Scholar
  11. 11.
    Poli, R.: Analysis of the publications on the applications of particle swarm optimisation. J. Artif. Evol. App., 4:1–4:10 (January 2008)Google Scholar
  12. 12.
    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., Southampton, UK, pp. 567–572. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  13. 13.
    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
  14. 14.
    Zhang, X., Hu, W., Maybank, S., Li, X., Zhu, M.: Sequential particle swarm optimization for visual tracking. In: IEEE Int. Conf. on CVPR, pp. 1–8 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

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

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