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Statistical Gesture Recognition Through Modelling of Parameter Trajectories

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Gesture-Based Communication in Human-Computer Interaction (GW 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1739))

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Abstract

The recognition of human gestures is a challenging problem that can contribute to a natural man–machine interface. In this paper, we present a new technique for gesture recognition. Gestures are modelled as temporal trajectories of parameters. Local sub-sequences of these trajectories are extracted and used to define an orthogonal space using principal component analysis. In this space the probabilistic density function of the training trajectories is represented by a multidimensional histogram, which builds the basis for the recognition. Experiments on three different recognition problems show the general utility of the approach.

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References

  1. T. S. Huand and V. I. Pavlović. Hand gesture modelling, analysis and synthesis. In International Conference on Face and Gesture Recognition FG’95, pp 73–79, Zurich, June 1995.

    Google Scholar 

  2. W. T. Freeman, K. Tanaka, J. Ohta, and K. Kyuma. Computer vision for computer games. In Irfan Essa, editor, FG’96, pp 100–105, Kilington, USA, Oct. 1996.

    Google Scholar 

  3. V. I. Pavlović, G. A. Berry, and T. S. Huang. Fusion of audio and visual information for use in human-computer interaction. In Proceedings of Perceptual User Interface, PUI’97, pp 68–70, Banff, Alberta, Canada, Oct. 1997.

    Google Scholar 

  4. T. Starner and A. Pentland. Real-time amercian sign language recognition form video using hidden markov model. Technical Report 375, MIT, Media Laboratory, 1995.

    Google Scholar 

  5. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland. Pfinder: Real-time tracking of tge human body. In FG’96, pp 51–56, Killington, USA, Oct. 1996.

    Google Scholar 

  6. T. J. Darrell and A. P. Pentland. Recognition of space-time gestures using distributed representation. Technical Report 197, MIT, Media Laboratory, 1992.

    Google Scholar 

  7. J. W. Davis. Appearance-based motion recognition of human actions. Technical Report 387, MIT, Media Laboratory, 1996.

    Google Scholar 

  8. S. Gutta, I. F. Imam, and H. Wechsler. Hand gesture recognition using ensembles of raidal basis function (rbf) networks and decision trees. International Journal of Pattern Recognition and Artificial Intelligence, 11(6):845–872, 1997.

    Article  Google Scholar 

  9. R. Erenshteyn, P. Laskov, R. Foulds, L. Messing, and G. Stern. Recognition approach to gesture language understanding. In ICPR’96, pp 431–435, Vienna, Austria, 1996.

    Google Scholar 

  10. J. L. Crowley and F. Bérard. Multi-modal tracking of faces for video communications. In CVPR’97, pp 640–645, San Juan, Puerto Rico, June 1997.

    Google Scholar 

  11. 3Com. PalmPilot, Users Manual.

    Google Scholar 

  12. J. Martin, D. Hall, and J. L. Crowley. Statistical recognition of parameter trajectories for hand gestures and face expressions. In ECCVWorkshop on Perception of Human Actions, Freiburg, Germany, June 1998.

    Google Scholar 

  13. B. Schiele. Reconnaissance d’Objets utilisant des Histogrammes Multidimentsionnels de Champs Réceptifs. PhD thesis, INP Grenoble, July 1997. In French.

    Google Scholar 

  14. I. Sirovich and M. Kirby. Low-dimensional procedure for the caracterization of human faces. Journal of Optical Society of America A, 4(3):519–524, March 1987.

    Article  Google Scholar 

  15. M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Neuroscience, 3(1):71–86, 1991.

    Google Scholar 

  16. A. Lux and B. Zoppis. An Experimental Multi-language Environment for the Development of Intelligent Robot Systems. In 5 th International Symposium on Intelligent Robotic Systems, SIRS’97, pp 169–174, 1997. Details at http://www-prima.imag.fr/Ravi/.

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Author information

Authors and Affiliations

  1. Projet PRIMA — Lab. GRAVIR–IMAG INRIA Rhônes–Alpes, 655, avenue de l’Europe, 38330, Montbonnot Saint Martin, France

    J. Martin, D. Hall & J. L. Crowley

Authors
  1. J. Martin
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  2. D. Hall
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  3. J. L. Crowley
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Editor information

Editors and Affiliations

  1. LIMSI-CNRS, BP 133, 91403, Orsay cedex, France

    Annelies Braffort , Rachid Gherbi , Sylvie Gibet  & Daniel Teil , ,  & 

  2. Université Paris Sud, LPM, 9140, Orsay, France

    James Richardson

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© 1999 Springer-Verlag Berlin Heidelberg

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Martin, J., Hall, D., Crowley, J.L. (1999). Statistical Gesture Recognition Through Modelling of Parameter Trajectories. In: Braffort, A., Gherbi, R., Gibet, S., Teil, D., Richardson, J. (eds) Gesture-Based Communication in Human-Computer Interaction. GW 1999. Lecture Notes in Computer Science(), vol 1739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46616-9_12

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  • DOI: https://doi.org/10.1007/3-540-46616-9_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66935-7

  • Online ISBN: 978-3-540-46616-1

  • eBook Packages: Springer Book Archive

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