Fast and Accurate Hand Pose Detection for Human-Robot Interaction

  • Luis Antón-Canalís
  • Elena Sánchez-Nielsen
  • Modesto Castrillón-Santana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3522)


Enabling natural human-robot interaction using computer vision based applications requires fast and accurate hand detection. However, previous works in this field assume different constraints, like a limitation in the number of detected gestures, because hands are highly complex objects difficult to locate. This paper presents an approach which integrates temporal coherence cues and hand detection based on wrists using a cascade classifier. With this approach, we introduce three main contributions: (1) a transparent initialization mechanism without user participation for segmenting hands independently of their gesture, (2) a larger number of detected gestures as well as a faster training phase than previous cascade classifier based methods and (3) near real-time performance for hand pose detection in video streams.


Video Stream Gesture Recognition Hand Gesture Training Stage Tracker Module 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Viola, P., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. IEEE Computer Vision and Pattern Recognition 1, 511–518 (2001)Google Scholar
  2. 2.
    Isard, M., Blake, A.: Condensation - Conditional density propagation for visual tracking. International. Journal of Computer Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  3. 3.
    Brethes, L., Menezes, P., Lerasle, L., Hayet, J.: Face tracking hand gesture recognition for human-robot interaction. In: Brethes, L., Menezes, P., Lerasle, L., Hayet, J. (eds.) IEEE International Conference on Robotics and Automation, New Orleans, April 26-May 1 (2004)Google Scholar
  4. 4.
    Triesch, J., von der Malsburg, C.: A System for Person-Independent Hand Posture Recognition against complex backgrounds. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(12), 1449–1453 (2001)CrossRefGoogle Scholar
  5. 5.
    Rehg, J.M., Kanade, T.: Visual tracking of high DOF articulated structures: an application to human hand tracking. In: 3rd Proc. European Conference on Computer Vision, vol. II, pp. 35–46Google Scholar
  6. 6.
    Spengler, M., Schiele, B.: Towards robust multi-cue integration for visual tracking. Machine Vision and Applications 14, 50–58 (2003)CrossRefGoogle Scholar
  7. 7.
    Stenger, B., Thayananthan, A., Torr, P., Cipolla, R.: Hand Pose Estimation using Hierarchical Detection. In: Sebe, N., Lew, M., Huang, T.S. (eds.) ECCV/HCI 2004. LNCS, vol. 3058, pp. 102–112. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Kösch, M., Turk, M.: Robust hand detection. In: 6th IEEE International Conference on Automatic Face and Gesture Recognition, Korea, May 17-19 (2004)Google Scholar
  9. 9.
    Barreto, J., Menezes, P., Dias, J.: Human-Robot Interation based on Haar-like Features and Eigenfaces. In: IEEE International Conference on Robotics and Automation, New Orleans, April 26-May 1 (2004)Google Scholar
  10. 10.
    Kösch, M., Turk, M.: Analysis of Rotational Robustness of Hand Detection with a Viola-Jones Detector. In: IAPR International Conference of Pattern Recognition (2004)Google Scholar
  11. 11.
    Kruppa, H., Catrillón, M., Schiele, B.: Fast and Robust Face Finding via Local Context. In: Joint IEEE International Workshop on VS_PETS, Nice, France (2003)Google Scholar
  12. 12.
    Rogers Peck, S.: Atlas of Human Anatomy for the Artist. Oxford University Press, Inc, USA (1982) ISBN: 01950309858Google Scholar
  13. 13.
    Guerra Artal, C.: Contributions to visual precategoric tracking. Phd thesis, University of Las Palmas G.C. (2002)Google Scholar
  14. 14.
    Edelman, S.: Representation and Recognition in Vision. The MIT Press, Cambridge (1999)Google Scholar
  15. 15.
    Triesch, J.: Hand Posture Database I, II,

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Luis Antón-Canalís
    • 1
  • Elena Sánchez-Nielsen
    • 2
  • Modesto Castrillón-Santana
    • 1
  1. 1.Institute of Intelligent Systems and Numerical Applications in EngineeringCampus Universitario de TafiraGran CanariaSpain
  2. 2.Department of S.O.R. and ComputationUniversity of La LagunaSpain

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