Corpus ID: 14761528

Human-Computer Interaction Using Robust Gesture Recognition

@inproceedings{Endler2014HumanComputerIU,
  title={Human-Computer Interaction Using Robust Gesture Recognition},
  author={M. Endler and O. Lobachev and M. Guthe},
  year={2014}
}
  • M. Endler, O. Lobachev, M. Guthe
  • Published 2014
  • Computer Science
  • We present a detector cascade for robust real-time tracking of hand movements on consumer-level hardware. We adapt existing detectors to our setting: Haar, CAMSHIFT, shape detector, skin detector. We use all these detectors at once. Our main contributions are: first, utilization of bootstrapping: Haar bootstraps itself, then its results are used to bootstrap the other filters; second, the usage of temporal filtering for more robust detection and to remove outliers; third, we adapted the… CONTINUE READING

    References

    SHOWING 1-10 OF 21 REFERENCES
    Computer Vision Face Tracking For Use in a Perceptual User Interface
    • 1,708
    • Highly Influential
    • PDF
    Vision based hand gesture recognition for human computer interaction: a survey
    • 969
    Hand gesture recognition using a neural network shape fitting technique
    • 247
    • PDF
    Rapid object detection using a boosted cascade of simple features
    • P. Viola, Michael J. Jones
    • Computer Science
    • Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
    • 2001
    • 16,699
    • PDF
    Face Detection: A Survey
    • 1,616
    • PDF
    Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection
    • 907
    • PDF
    A Survey on Pixel-Based Skin Color Detection Techniques
    • 1,187
    • PDF
    Object recognition from local scale-invariant features
    • D. Lowe
    • Mathematics, Computer Science
    • Proceedings of the Seventh IEEE International Conference on Computer Vision
    • 1999
    • 15,143
    • PDF
    Face Detection using Combined Skin Color Detector and Template Matching Method
    • 38
    • Highly Influential
    • PDF