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International Symposium on Visual Computing

ISVC 2006: Advances in Visual Computing pp 151–160Cite as

Efficient Motion Search in Large Motion Capture Databases

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4291))

Abstract

Large human motion databases contain variants of natural motions that are valuable for animation generation and synthesis. But retrieving visually similar motions is still a difficult and time-consuming problem. This paper provides methods for identifying visually and numerically similar motions in a large database given a query of motion segment. We propose an efficient indexing strategy that represents the motions compactly through a preprocessing. This representation scales down the range of searching the database. Motions in this range are possible candidates of the final matches. For detailed comparisons between the query and the candidates, we propose an algorithm that compares the motions’ curves swiftly. Our methods can apply to large human motion databases and achieve high performance and accuracy compared with previous work. We present experimental results on testing a database of about 2.9 million frames, or about 27 hours of motions played at 30 Hz.

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References

  1. Kovar, L., Gleicher, M.: Automated extraction and parameterization of motions in large data sets. In: Proceedings of ACM SIGGRAPH 2004, pp. 559–568 (2004)

    Google Scholar 

  2. Muller, M., Roder, T., Glausen, M.: Efficient content-based retrieval of motion capture data. In: Proceedings of ACM SIGGRAPH 2005, pp. 677–685 (2005)

    Google Scholar 

  3. Liu, F., Zhuan, Y., Wu, F., Pan, Y.: 3d motion retrieval with motion index tree. Computer Vision and Image Understanding 92, 265–284 (2003)

    Article  Google Scholar 

  4. Graphics Lab, Carnegie-Mellon University (Carnegie-Mellon MoCap Database), http://mocap.cs.cmu.edu

  5. Witten, I.H., Moffat, A., Bell, T.C.: Managing Gigabytes. Morgan Kaufmann Publishers, San Francisco (1999)

    Google Scholar 

  6. Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.): CIVR 2003. LNCS, vol. 2728. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  7. Clausen, M., Kurth, F.: A unified approach to content-based and fault tolerant music recognition. IEEE Transactions on Multimedia 6, 717–731 (2004)

    Article  Google Scholar 

  8. Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: Proceedings of 1994 ACM SIGMOD International Conference on Management of Data, pp. 419–429 (1994)

    Google Scholar 

  9. Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993)

    Google Scholar 

  10. Chan, K., Fu, W.: Efficient time series matching by wavelets. In: Proceedings of the 15th IEEE International Conference on Data Engineering, pp. 126–133 (1999)

    Google Scholar 

  11. Keogh, E., Chakrabarti, K., Mehrotra, S., Pazzani, M.: Locally adaptive dimensionality reduction for indexing large time series databases. In: Proceedings of 1994 ACM SIGMOD International Conference on Management of Data, pp. 151–162 (2001)

    Google Scholar 

  12. Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Proceedings of 1994 ACM SIGMOD International Conference on Management of Data, pp. 47–57 (1984)

    Google Scholar 

  13. Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., Keogh, E.: Indexing multi-dimensional time-series with support for multiple distance measures. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 216–225 (2003)

    Google Scholar 

  14. Lee, J., Chai, J., Reitsma, P., Hodgins, J., Pollard, N.: Interactive control of avatars animated with human motion data. In: Proceedings of ACM SIGGRAPH 2002, pp. 491–500 (2002)

    Google Scholar 

  15. Arikan, O., Forsyth, D.A.: Interactive motion generation from examples. In: Proceedings of ACM SIGGRAPH 2002, pp. 483–490 (2002)

    Google Scholar 

  16. Wang, J., Bodenheimer, B.: An evaluation of a cost metric for selecting transitions between motion segments. In: Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation 2003, pp. 232–238 (2003)

    Google Scholar 

  17. Brejova, B., Brown, D., Vinar, T.: Vector seeds: An extension to spaced seeds. Journal of Computer and System Sciences 70, 364–380 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  18. Ma, B., Tromp, J., Li, M.: Patternhunter: faster and more sensitive homology search. Bioinformatics 18, 440–445 (2002)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Waterloo,  

    Yi Lin

Authors
  1. Yi Lin
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Intel, 95052, Santa Clara, CA, USA

    Ara Nefian

  7. University of California, 430, Computer Science Building, 92697-3425, Irvine, CA, USA

    Gopi Meenakshisundaram

  8. Institute for Data Analysis and Visualization, P.O. Box

    Valerio Pascucci

  9. Department of Computer Science and Engineering, Czech Technical University in Prague, Czech

    Jiri Zara

  10. Rockwell Scientific, 1049 Camino Dos Rios, 91360, Thousand Oaks, CA, USA

    Jose Molineros

  11. Computer Graphics Group, Bielefeld University, D-33501, Bielefeld, Germany

    Holger Theisel

  12. Hewlett Packard Labs, Palo Alto, CA, USA

    Tom Malzbender

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

Lin, Y. (2006). Efficient Motion Search in Large Motion Capture Databases. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_16

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  • DOI: https://doi.org/10.1007/11919476_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48628-2

  • Online ISBN: 978-3-540-48631-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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