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Tracking End-Effectors for Marker-Less 3D Human Motion Estimation in Multi-view Image Sequences

  • Wenzhong Wang
  • Zhaoqi Wang
  • Xiaoming Deng
  • Bin Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8008)

Abstract

We propose to track the end-effectors of human body, and use them as kinematic constraints for reliable marker-less 3D human motion tracking. In the presented approach, we track the end-effectors using particle filtering. The tracked results are then combined with image features for 3D full pose tracking. Experimental results verified that the inclusion of end-effectors’ constraints improves the tracking performances.

Keywords

end-effectors motion tracking particle filtering 

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References

  1. 1.
    Moeslund, T.B., et al. (eds.): Visual analysis of humans: looking at people. Springer (2011)Google Scholar
  2. 2.
    Ganapathi, V., et al.: Real time motion capture using a single time-of-flight camera. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2010)Google Scholar
  3. 3.
    Pons-Moll, G., et al.: Outdoor human motion capture using inverse kinematics and von mises-fisher sampling. In: IEEE International Conference on Computer Vision, ICCV. IEEE (2011)Google Scholar
  4. 4.
    Hauberg, S., Pedersen, K.S.: Predicting articulated human motion from spatial processes. International Journal of Computer Vision 94(3), 317–334 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Baak, A., et al.: A data-driven approach for real-time full body pose reconstruction from a depth camera. In: IEEE International Conference on Computer Vision (ICCV). IEEE (2011)Google Scholar
  6. 6.
    Isard, M., Blake, A.: Condensation—conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  7. 7.
    Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997)CrossRefGoogle Scholar
  8. 8.
    Sigal, L., Balan, A.O., Black, M.J.: Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. International Journal of Computer Vision 87(1), 4–27 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wenzhong Wang
    • 1
  • Zhaoqi Wang
    • 2
  • Xiaoming Deng
    • 3
  • Bin Luo
    • 1
  1. 1.Computer Science DepartmentAnhui UniversityChina
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesChina
  3. 3.Institute of SoftwareChinese Academy of SciencesChina

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