Skip to main content

Ballistic Hand Movements

  • Conference paper
Articulated Motion and Deformable Objects (AMDO 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4069))

Included in the following conference series:

Abstract

Common movements like reaching, striking, etc. observed during surveillance have highly variable target locations. This puts appearance-based techniques at a disadvantage for modelling and recognizing them. Psychological studies indicate that these actions are ballistic in nature. Their trajectories have simple structures and are determined to a great degree by the starting and ending positions. We present an approach for movement recognition that explicitly considers their ballistic nature. This enables the decoupling of recognition from the movement’s trajectory, allowing generalization over a range of target-positions. A given movement is first analyzed to determine if it is ballistic. Ballistic movements are further classified into reaching, striking, etc. The proposed approach was tested with motion capture data obtained from the CMU MoCap database.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Smyth, I., Wing, M. (eds.): The Psychology of Human Movement. Academic Press Inc., Orlando, FL 32887 (1984)

    Google Scholar 

  2. Marteniuk, R.G., MacKenzie, C.L., Jeannerod, M., Athenes, S., Dugas, C.: Constraints on human arm movement trajectories. Canadian Jnl. Psychology 41, 365–378 (1987)

    Article  Google Scholar 

  3. Asatryan, D.G., Fel’dman, A.G.: Functional tuning of the nervous system with control of movement or maintenance of a steady posture. Biophysics 1, 925–935 (1965)

    Google Scholar 

  4. Cooke, J.D.: The organization of simple, skilled movements. In: Stelmach, G.E., Requin, J. (eds.) Tutorials in Motor Behavior, pp. 199–211 (1980)

    Google Scholar 

  5. Gavrila, D.M.: The visual analysis of human movement: A survey. Computer Vision and Image Understanding 73, 82–98 (1999)

    Article  MATH  Google Scholar 

  6. Wilson, A.D., Bobick, A.F.: Parametric hidden markov models for gesture recognition. IEEE Trans. Pattern Anal. and Machine Intell. 21, 884–900 (1999)

    Article  Google Scholar 

  7. Parameswaran, V., Chellappa, R.: View invariants for human action recognition. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2003), vol. 2, pp. 613–619 (2003)

    Google Scholar 

  8. Arikan, O., Forsyth, D.A., O’Brien, J.F.: Motion synthesis from annotations. ACM Trans. Graph. 22, 402–408 (2003)

    Article  MATH  Google Scholar 

  9. Yilmaz, A., Shah, M.: Recognizing human action in videos acquired by uncaliberated moving cameras. In: Proc. IEEE Int’l Conf. Computer Vision (ICCV 2005) (2005)

    Google Scholar 

  10. Elgammal, A., Shet, V.D., Yacoob, Y., Davis, L.S.: Learning dynamics for exemplar-based gesture recognition. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2003), June 18-20, 2003, vol. 1, pp. 571–578 (2003)

    Google Scholar 

  11. Yilmaz, A., Shah, M.: Actions as objects: A nover action representation. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2005) (2005)

    Google Scholar 

  12. Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. and Machine Intell. 23, 257–267 (2001)

    Article  Google Scholar 

  13. Johansson, G.: Visual perception of biological motion and a model for its analysis. Perception and Psychophysics 14, 201–211 (1973)

    Article  Google Scholar 

  14. Bregler, C.: Learning and recognizing human dynamics in video sequences. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 1997) (1997)

    Google Scholar 

  15. Pavlovic, V., Rehg, J.M., MacCormick, J.: Learning switching linear models for human motion. In: Proc. Neural Information Processing Systems (NIPS 2000), pp. 981–987 (2000)

    Google Scholar 

  16. Ren, L., Patrick, A., Efros, A.A., Hodgins, J.K., Rehg, J.M.: A data-driven approach to quantifying natural human motion. ACM Trans. Graph. 24, 1090–1097 (2005)

    Article  Google Scholar 

  17. Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 77, 257–286 (1989)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Prasad, V.S.N., Kellokumpu, V., Davis, L.S. (2006). Ballistic Hand Movements. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2006. Lecture Notes in Computer Science, vol 4069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11789239_16

Download citation

  • DOI: https://doi.org/10.1007/11789239_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36031-5

  • Online ISBN: 978-3-540-36032-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics