A Generic Approach to Design and Querying of Multi-purpose Human Motion Database

  • Wiktor Filipowicz
  • Piotr Habela
  • Krzysztof Kaczmarski
  • Marek Kulbacki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6374)


The advancement of motion recording, analysis and synthesis techniques, together with the standardization of respective data formats, constitutes a solid foundation for research based on motion data sets in the context of various disciplines. However, most of the motion data sets available offer groups of files as acquired from the motion capture systems. The problem with such data is that it usually represents a single viewpoint, its context varies and is more or less implicit, and the lack of functionality implemented atop of such data set limits the data analysis and search potential. This encourages us to look at this problem domain from the database management systems (DBMS) state of the art point of view. In this paper, we outline some important aspects of applying a DBMS to motion data with the aim to provide a highly universal, extensible, shareable and searchable resource. To avoid being locked into a specific area of application, we take a very abstract view of the data and attempt to assure, a versatility and genericity of the resulting system.


Motion capture multimedia databases structured data extendibility database management 


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  1. 1.
    Sigal, L., Balan, A., Black, M.: Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. IJCV 87(1), 4–27 (2010)CrossRefGoogle Scholar
  2. 2.
    Forbes, K., Fiume, E.: An efficient search algorithm for motion data using weighted pca. In: 2005 ACM SIGGRAPH/Eurographics SCA, pp. 67–76 (July 2005)Google Scholar
  3. 3.
    Chiu, C.Y., Chao, S.P., Wu, M.Y., Yang, S.N.: Efficient content-based retrieval for motion capture data. Journal of Visual Communication and Image Representation 15, 446–466 (2004)CrossRefGoogle Scholar
  4. 4.
    Kovar, L., Gleicher, M.: Automated extraction and parameterization of motions in large data sets. ACM Transactions on Graphics 23(3), 559–568 (2004)CrossRefGoogle Scholar
  5. 5.
    Demuth, B., Müller, M., Eberhardt, B.: An information retrieval system for motion capture data. In: Lalmas, M., MacFarlane, A., Rüger, S.M., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds.) ECIR 2006. LNCS, vol. 3936, pp. 373–384. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Ren, L.: Statistical Analysis of Natural Human Motion for Animation. PhD thesis, CMU (2007)Google Scholar
  7. 7.
    Assa, J., Caspi, Y., Cohen-Or, D.: Action synopsis: pose selection and illustration. ACM Transactions on Graphics 24(3), 667–676 (2005)CrossRefGoogle Scholar
  8. 8.
    Sakamoto, Y., Kuriyama, S., Kaneko, T.: Motion map: image-based retrieval and segmentation of motion data. In: 2004 ACM SIGGRAPH/Eurographics SCA, pp. 259–266 (July 2004)Google Scholar
  9. 9.
    Liu, G., Zhang, J., Wang, W., McMillan, L.: A system for analyzing and indexing human-motion databases. In: Proceedings of the ACM SIGMOD, pp. 924–926 (2005)Google Scholar
  10. 10.
    Gaeorgia Tech: HID Database,
  11. 11.
    CMU: Carnegie-Mellon Mocap Database,
  12. 12.
    CASIA Gait Database,
  13. 13.
    Animeeple Animation Software,
  14. 14.
  15. 15.
    Prabhakaran, B.: Multimedia Database Management Systems. Springer, Heidelberg (1996)Google Scholar
  16. 16.
    Global Alliance WS-Resource Framework,

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Wiktor Filipowicz
    • 1
  • Piotr Habela
    • 1
  • Krzysztof Kaczmarski
    • 2
  • Marek Kulbacki
    • 1
  1. 1.Polish-Japanese Institute of Information TechnologyWarsawPoland
  2. 2.Warsaw University of TechnologyWarsawPoland

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