Semantic Segmentation of Motion Capture Using Laban Movement Analysis

  • Durell Bouchard
  • Norman Badler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4722)


Many applications that utilize motion capture data require small, discrete, semantic segments of data, but most motion capture collection processes produce long sequences of data. The smaller segments are often created from the longer sequences manually. This segmentation process is very laborious and time consuming. This paper presents an automatic motion capture segmentation method based on movement qualities derived from Laban Movement Analysis (LMA). LMA provides a good compromise between high-level semantic features, which are difficult to extract for general motions, and low-level kinematic features which, often yield unsophisticated segmentations. The LMA features are computed using a collection of neural networks trained with temporal variance in order to create a classifier that is more robust with regard to input boundaries. The actual segmentation points are derived through simple time series analysis of the LMA features.


Human motion motion capture motion segmentation  Laban Movement Analysis LMA 


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  1. 1.
    Barbič, J., Safonova, A., Pan, J.Y., Faloutsos, C., Hodgins, J.K., Pollard, N.S.: Segmenting motion capture data into distinct behaviors. In: GI 2004. Proceedings of Graphics Interface (2004)Google Scholar
  2. 2.
    Bregler, C.: Learning and recognizing human dynamics in video sequences. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 568–574 (1997)Google Scholar
  3. 3.
    Fod, A., Matari ć, M.J., Jenkins, O.C.: Automated derivation of primitives for movement classification. Autonomous Robots 12(1), 39–54 (2002)zbMATHCrossRefGoogle Scholar
  4. 4.
    Jenkins, O.C., Mataric, M.J.: Automated derivation of behavior vocabularies for autonomous humanoid motion. In: Proceedings of the second international joint conference on Autonomous agents and multiagent systems, pp. 225–232 (2003)Google Scholar
  5. 5.
    Jenkins, O.C., Matarić, M.J.: A spatio-temporal extension to isomap nonlinear dimension reduction. In: ACM International Conference Proceeding Series (2004)Google Scholar
  6. 6.
    Kahol, K., Tripathi, P., Panchanathan, S.: Automated gesture segmentation from dance sequences. In: Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference, pp. 883–888 (2004)Google Scholar
  7. 7.
    Lee, C.S., Elgammal, A.: Human motion synthesis by motion manifold learning and motion primitive segmentation. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2006. LNCS, vol. 4069, pp. 464–473. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Osaki, R., Shimada, M., Uehara, K.: A motion recognition method by using primitive motions. In: Proceedings of the Fifth Working Conference on Visual Database Systems: Advances in Visual Information Management, pp. 117–128 (2000)Google Scholar
  9. 9.
    Ruck, D.W., Rogers, S.K., Kabrisky, M.: Feature selection using a multilayer perceptron. Journal of Neural Network Computing 2(2), 40–48 (1990)Google Scholar
  10. 10.
    Shiratori, T., Nakazawa, A., Ikeuchi, K.: Rhythmic motion analysis using motion capture and musical information. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 89–94 (2003)Google Scholar
  11. 11.
    Starner, T., Pentland, A.: Visual recognition of american sign language using hidden markov models. Master’s thesis, Massachusetts Institute of Technology, Program in Media Arts and Sciences (1995)Google Scholar
  12. 12.
    Wang, T.S., Shum, H.Y., Xu, Y.Q., Zheng, N.N.: Unsupervised analysis of human gestures. In: IEEE Pacific Rim Conference on Multimedia, pp. 174–181 (2001)Google Scholar
  13. 13.
    Shum, H.-Y., Li, Y., Wang, T.: Motion texture: a two-level statistical model for character motion synthesis. In: Siggraph 2002, pp. 465–472 (2002)Google Scholar
  14. 14.
    Zhao, L., Badler, N.: Acquiring and validating motion qualities from live limb gestures. Journal of Graphical Models (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Durell Bouchard
    • 1
  • Norman Badler
    • 1
  1. 1.Center for Human Modeling and Simulation, University of Pennsylvania, 200 S. 33rd St. Philadelphia, PA 19104USA

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