Advertisement

Hand-Raising Gesture Detection with Lienhart-Maydt Method in Videoconference and Distance Learning

  • Tiago S. Nazaré
  • Moacir Ponti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

In video-conference and distance learning videos, the moment that someone makes a hand-raising gesture is relevant to be included in the video annotation. However, gesture recognition can be challenging in such scenarios. We propose a system to detect faces, the hand-raising gesture and annotate the video. The Lienhart-Maydt object detection method is used, in which each frame is classified. Then, the gesture is detected by analyzing intervals of frames. Our approach was tested in videos with several characteristics. The results show that our method can deal with illumination and background variations, is able to detect multiple gestures and it is robust to confusing gestures. Besides it allow the use of moving cameras.

Keywords

Video processing gesture detection video annotation 

References

  1. 1.
    Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)Google Scholar
  2. 2.
    Duan, X., Liu, H.: Detection of hand-raising gestures based on body silhouette analysis. In: IEEE Int. Conf. Robotics and Biomimetics, pp. 1756–1761 (2009)Google Scholar
  3. 3.
    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proc. 13th International Conference on Machine Learning (ICML 1996), pp. 148–156 (1996)Google Scholar
  4. 4.
    Kölsch, M., Turk, M.: Analysis of rotational robustness of hand detection with a viola-jones detector. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), vol. 3, pp. 107–110 (2004)Google Scholar
  5. 5.
    Kölsch, M., Turk, M.: Robust hand detection. In: 6th IEEE Int. Conf. on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 614–619 (2004)Google Scholar
  6. 6.
    Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: IEEE Int. Conf. Image Processing (ICIP), pp. 900–903 (2002)Google Scholar
  7. 7.
    Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 349–361 (2001)CrossRefGoogle Scholar
  8. 8.
    Qin, S., Zhu, X., Yang, Y., Jiang, Y.: Real-time hand gesture recognition from depth images using convex shape decomposition method. Journal of Signal Processing Systems, 1–12 (2013)Google Scholar
  9. 9.
    Seo, N.: Tutorial: OpenCV Haar training (rapid object detection with a cascade of boosted classifiers based on Haar-like features) (2011), http://tutorial-haartraining.googlecode.com/svn/trunk/data/negatives/
  10. 10.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. 2001 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2001), pp. 511–518 (2001)Google Scholar
  11. 11.
    Yao, J., Cooperstock, J.R.: Arm gesture detection in a classroom environment. In: Proc. 6th IEEE Work. Appl. Computer Vision, pp. 153–157 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tiago S. Nazaré
    • 1
  • Moacir Ponti
    • 1
  1. 1.Instituto de Ciências Matemáticas e de Computac̨ãoUniversidade de São PauloSão CarlosBrazil

Personalised recommendations