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Articulated Human Motion Tracking by Sequential Annealed Particle Swarm Optimization

  • Yi Li
  • Zhengxing Sun
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)

Abstract

In this paper, we present a novel generative method for articulated human motion tracking. The principle contribution is the development of a modified Particle Swarm Optimization (PSO) algorithm for pose optimization in latent space of human motion. There are three characteristics in the proposed method. Firstly, we learn the latent space of human motion using PCA and perform human motion analysis in this latent space, which results to be more efficient and accurate. Secondly, we introduce simulated annealing into traditional PSO. A new algorithm, termed annealed PSO (APSO) is designed for pose optimization, which can get global optimum solution more efficiently. Lastly, we apply APSO for human pose estimation. And a sequential APSO (SAPSO) method is proposed for motion tracking. Experimental results on different motion types and different image sequences show that our method achieves better results than state-of-art methods.

Keywords

Pose estimation Motion tracking Particle swarm optimization 

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References

  1. 1.
    Sminchisescu, C.: Human motion understanding, modeling, capture and animation. In: Kleete, R., Metaxas, D., Rosenhahn, B. (eds.) 3D Human Motion Analysis in Monocular Video, Techniques and Challenges. Springer (2007)Google Scholar
  2. 2.
    Mori, G., Malik, J.: Recovering 3D human body configurations using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1052–1062 (2006)CrossRefGoogle Scholar
  3. 3.
    Agarwal, A., Triggs, B.: Recovering 3-D human pose from monocular images. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 44–58 (2006)CrossRefGoogle Scholar
  4. 4.
    Zhao, X., Liu, Y.C.: Generative tracking of 3D human motion by hierarchical annealed genetic algorithm. Pattern Recognition 41(8), 2470–2483 (2008)zbMATHCrossRefGoogle Scholar
  5. 5.
    Isard, M., Blake, A.: Condensation: conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)CrossRefGoogle Scholar
  6. 6.
    Howe, N.R., Leventon, M.E., Freeman, W.T.: Bayesian Reconstruction of 3D human motion from single-camera video. In: Advances in Neural Information Processing Systems, pp. 820–826. IEEE Press, Denver (2000)Google Scholar
  7. 7.
    John, V., Trucco, E., Ivekovic, S.: Markerless human articulated tracking using hierarchical particle swarm optimization. Image and Vis. Comput. 28(11), 1530–1547 (2010)CrossRefGoogle Scholar
  8. 8.
    Sigal, L., Black, M.J.: HumanEva: Synchronized video and motion capture dataset for evaluation of articulated human motion. Int. J. Comput. Vis. 87(1), 4–27 (2010)CrossRefGoogle Scholar
  9. 9.
    Zhang, X.Q., Hu, W.M., Maybank, S., Xi, L.: Sequential particle swarm optimization for visual tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 23–28. IEEE Press, Anchorage (2008)Google Scholar
  10. 10.
    CMU Motion Capture database, http://mocap.cs.cmu.edu/
  11. 11.
    Ormoneit, D., Sidenbladh, H., Black, M.J., Hastie, T.: Learning and tracking cyclic human motion. In: Advances in Neural Information Processing Systems, pp. 894–900. IEEE Press, Vancouver (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yi Li
    • 1
  • Zhengxing Sun
    • 1
  1. 1.State Key Lab for Novel Software TechnologyNanjing UniversityP.R. China

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