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A Quick Filtering for Similarity Queries in Motion Capture Databases

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 5879)

Abstract

A similarity retrieval of motion capture data has received substantial attention in recent years. In this paper, we focus on feature extraction and quick filtering methods in the similarity retrieval system. A representation of motion capture data is joint angles, which can distinguish different human body poses. We propose a new technique for dimensionality reduction based the average and variance of joint angles. Our dimensionality reduction is simple to understand and implement. In experiments, twenty dance motion clips each of which is different in length and style, are used in the test data set with a total of 60,000 frames. The results of our quick filtering show an achievement on the recall and precision up to 100% and 70%, respectively.

Keywords

  • Similarity Retrieval
  • Motion Capture
  • Body Motion Data
  • Dimension Reduction
  • Minimal Bounding Envelop
  • Feature Extraction

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  • DOI: 10.1007/978-3-642-10467-1_35
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References

  1. Yoshimura, M., Murasato, H., Kai, T., Kuromiya, A., Yokoyama, K., Hachimura, K.: Analysis of japanese dance movements using motion capture system. Syst. Comput. Japan 37(1), 71–82 (2006)

    CrossRef  Google Scholar 

  2. Yoshimura, M., Hachimura, K., Sakata, M., Yuuka: Comparison of structural variables with spatio-temporal variables concerning the identifiability of okuri class and player in japanese traditional dancing. In: 18th International Conference on Pattern Recognition, 2006. ICPR 2006, vol. 3, pp. 308–311 (2006)

    Google Scholar 

  3. Hachimura, K.: Digital archiving of dancing by using motion capture. Technical report, IPSJ SIG Technical reports (2007)

    Google Scholar 

  4. Choi, W., Isaka, T., Sakata, M., Tsuruta, S., Hachimura, K.: Quantification of dance movement by simultaneous measurement of body motion and biophysical information. International Journal of Automation and Computing 4, 1–7 (2007)

    CrossRef  Google Scholar 

  5. Sonoda, M., Tsuruta, S., Yoshimura, M., Hachimura, K.: Segmentation of dancing movement by extracting features from motion capture data. Journal of the Institute of Image Electronics Engineers of Japan 37(3), 303–311 (2008)

    Google Scholar 

  6. Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: KDD 2000: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 285–289. ACM, New York (2000)

    CrossRef  Google Scholar 

  7. Okada, S.: Motion recognition based on dynamic-time warping method with self-organizing incremental neural network. In: ICPR 2008: Proceedings of 19th International Conference on Pattern Recognition, pp. 1–4 (2008)

    Google Scholar 

  8. Yaniv, R., Burshtein, D.: An enhanced dynamic time warping model for improved estimation of dtw parameters. IEEE Transactions on Speech and Audio Processing 11(3), 216–228 (2003)

    CrossRef  Google Scholar 

  9. Chiu, C.Y., Chao, S.P., Wu, M.Y., Yang, S.N., Lin, H.C.: Content-based retrieval for human motion data. Journal of visual communication and image representation 15(3), 446–466 (2004)

    CrossRef  Google Scholar 

  10. Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley & Sons, Chichester (2001)

    MATH  Google Scholar 

  11. Müller, M., Röder, T., Clausen, M.: Efficient content-based retrieval of motion capture data. ACM Trans. Graph. 24(3), 677–685 (2005)

    CrossRef  Google Scholar 

  12. Barbič, J., Safonova, A., Pan, J.Y., Faloutsos, C., Hodgins, J.K., Pollard, N.S.: Segmenting motion capture data into distinct behavior. In: Proceedings of Graphics Interface, vol. 62, pp. 185–194. ACM, New York (2004)

    Google Scholar 

  13. Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., Keogh, E.: Indexing multidimensional time-series. The VLDB Journal 15(1), 1–20 (2006)

    CrossRef  Google Scholar 

  14. Nakata, T.: Temporal segmentation and recognition of body motion data based on inter-lib correlation analysis. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1383–1388 (2007)

    Google Scholar 

  15. Keogh, E.J., Pazzani, M.J.: A simple dimensionality reduction technique for fast similarity search in large time series databases. In: PADKK 2000: Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications, London, UK, pp. 122–133. Springer, Heidelberg (2000)

    CrossRef  Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Choensawat, W., Choi, W., Hachimura, K. (2009). A Quick Filtering for Similarity Queries in Motion Capture Databases. In: Muneesawang, P., Wu, F., Kumazawa, I., Roeksabutr, A., Liao, M., Tang, X. (eds) Advances in Multimedia Information Processing - PCM 2009. PCM 2009. Lecture Notes in Computer Science, vol 5879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10467-1_35

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  • DOI: https://doi.org/10.1007/978-3-642-10467-1_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10466-4

  • Online ISBN: 978-3-642-10467-1

  • eBook Packages: Computer ScienceComputer Science (R0)