Advertisement

Segmentation of Human Motion Sequence Based on Motion Primitives

  • Yu-ting Zhao
  • Yao Wang
  • Shuang Wu
  • Xiao-jun Li
  • Hua Qin
  • Taijie Liu
  • Linghua Ran
  • Jianwei NiuEmail author
Conference paper

Abstract

Motion capture data could describe human motion precisely. It’s anticipated some uncapturable human motions may be generated through easily capturable motions. A method was introduced to segment motion captured data, which could cut a long motion sequence into some unique motion primitives. This method involved 14 joints of body hierarchy. Singular Value Decomposition (SVM) was used to determine how motion data dimension changed, which could identify the segmentation frame. The validity of method was verified with an 8,401 frames motion sequence from Carnegie Mellon University Motion Capture Database, and it turned out to be valid in accordance with human intuitive judgment.

Keywords

Motion capture Motion segment Motion primitive Singular value decomposition (SVM) 

Notes

Acknowledgements

This research is supported by National Key R&D Program of China (2017YFF0206602) and Special funds for the basic R&D undertakings by welfare research institutions (522016Y-4680). The authors also appreciate the support from General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China (201510042), the State Scholarship Fund from China Scholarship Council (201208110144), the National Natural Science Foundation of China (51005016), and Fundamental Research Funds for the Central Universities, China (FRF-TP-14-026A2).

References

  1. 1.
    M. Pomplun, M. Mataric, Evaluation metrics and results of human arm Movement imitation, in Proceedings of the First IEEE-RAS International Conference on Humanoid Robots (2000)Google Scholar
  2. 2.
    O. Arikan, D. Forsyth, Interactive motion generation from examples, in Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, vol. 21, no. 3 (2002), pp. 483–490Google Scholar
  3. 3.
    K. Forbes, E. Fiume, An efficient search algorithm for motion data using weighted PCA, in Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer animation (2005), pp. 67–76Google Scholar
  4. 4.
    T.S. Wang, N.N. Zheng, Y.X. Xu, X.Y. Shen, Unsupervised cluster analysis of human motion. J. Softw. 14(2), 209–213 (2003)Google Scholar
  5. 5.
    K. Kanav, Gesture segmentation in complex motion sequences, in Proceedings IEEE International Conference on Image Processing (1996), pp. 94–99Google Scholar
  6. 6.
    M.J. Wang, Virtual person motion synthesis technology and its engineering application research. Thesis of National University of Defense Technology (2010)Google Scholar
  7. 7.
    M. Müller, T. Röder, Motion templates for automatic classification and retrieval of motion capture data, in Eurographics/ACM SIGGRAPH Symposium on Computer Animation (2006), pp. 137–146Google Scholar
  8. 8.
    J. Barbič, A. Safonova, Jia-Yu Pan, C. Faloutsos, Segmenting motion capture data into distinct behaviors, in Proceedings of Graphics Interface (2004), pp 185–194Google Scholar
  9. 9.
    https://www.mocap.cs.cmu.edu/. Accessed 31 Mar 2018

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yu-ting Zhao
    • 1
  • Yao Wang
    • 1
  • Shuang Wu
    • 1
  • Xiao-jun Li
    • 2
  • Hua Qin
    • 3
  • Taijie Liu
    • 4
  • Linghua Ran
    • 4
  • Jianwei Niu
    • 1
    Email author
  1. 1.School of Mechanical EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.Xi’an Research Institute of Hi-TechXi’anChina
  3. 3.School of Mechanical-electronic and Automobile EngineeringBeijing University of Civil Engineering and ArchitectureBeijingChina
  4. 4.China National Institute of StandardizationBeijingChina

Personalised recommendations