Advertisement

Model-Based 3D Human Motion Capture Using Global-Local Particle Swarm Optimizations

  • Tomasz Krzeszowski
  • Bogdan Kwolek
  • Konrad Wojciechowski
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

We present an approach for tracking the articulated motion of humans using image sequences obtained from multiple calibrated cameras. A 3D human body model composed of eleven segments, which allows both rotation at joints and translation, is utilized to estimate the pose. We assume that the initial pose estimate is available. A modified swarm intelligence based searching scheme is utilized to perform motion tracking. At the beginning of each optimization cycle, we estimate the pose of the whole body and then we refine locally the limb poses using smaller number of particles. The results that were achieved in our experiments are compared with those produced by other state-of-the-art methods, with analyses carried out both through qualitative visual evaluations as well as quantitatively by the use of the motion capture data as ground truth. They indicate that our method outperforms the algorithm based on the ordinary particle swarm optimization.

Keywords

Particle Swarm Optimization Motion Capture Motion Tracking Motion Capture Data Particle Swarm Optimiza 
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.
    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
  2. 2.
    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
  3. 3.
    Krzeszowski, T., Kwolek, B., Wojciechowski, K.: Articulated body motion tracking by combined particle swarm optimization and particle filtering. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6374, pp. 147–154. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Poppe, R.: Vision-based human motion analysis: An overview. Comp. Vision and Image Understanding 108, 4–18 (2007)CrossRefGoogle Scholar
  5. 5.
    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, vol. I, pp. 390–397 (2005)Google Scholar
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

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

Personalised recommendations