Article

Real time hand gesture recognition including hand segmentation and tracking

Authors:

Thomas Coogan,

George Awad,

Junwei Han,

Alistair SutherlandAuthors Info & Claims

ISVC'06: Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I

Pages 495 - 504

https://doi.org/10.1007/11919476_50

Published: 06 November 2006 Publication History

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Abstract

In this paper we present a system that performs automatic gesture recognition. The system consists of two main components: (i) A unified technique for segmentation and tracking of face and hands using a skin detection algorithm along with handling occlusion between skin objects to keep track of the status of the occluded parts. This is realized by combining 3 useful features, namely, color, motion and position. (ii) A static and dynamic gesture recognition system. Static gesture recognition is achieved using a robust hand shape classification, based on PCA subspaces, that is invariant to scale along with small translation and rotation transformations. Combining hand shape classification with position information and using DHMMs allows us to accomplish dynamic gesture recognition.

References

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Cited By

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Brock HLaw FNakadai KNagashima Y(2020)Learning Three-dimensional Skeleton Data from Sign Language VideoACM Transactions on Intelligent Systems and Technology10.1145/337755211:3(1-24)Online publication date: 17-Apr-2020
https://dl.acm.org/doi/10.1145/3377552
Lim KTan ALee CTan S(2019)Isolated sign language recognition using Convolutional Neural Network hand modelling and Hand Energy ImageMultimedia Tools and Applications10.1007/s11042-019-7263-778:14(19917-19944)Online publication date: 2-Aug-2019
https://dl.acm.org/doi/10.1007/s11042-019-7263-7
Tsagaris ATrigkas D(2018)Mobile Gesture RecognitionProceedings of the International Conference on Machine Vision and Applications10.1145/3220511.3220519(13-17)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3220511.3220519
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Index Terms

Real time hand gesture recognition including hand segmentation and tracking
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision tasks
        Scene understanding
  2. Machine learning
    1. Machine learning algorithms
      1. Feature selection

Index terms have been assigned to the content through auto-classification.

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Information

Published In

ISVC'06: Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I

November 2006

916 pages

ISBN:3540486283

Editors:
George Bebis
Department of Computer Science and Engineering, University of Nevada, Reno
,
Richard Boyle
Department of Computer Science and Engineering, NASA Ames Research Center, Moffett Field, CA
,
Bahram Parvin
Department of Computer Science and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA
,
Darko Koracin
Department of Computer Science and Engineering, Desert Research Institute, Reno, NV
,
Paolo Remagnino
Digital Image Research Center, Kingston University, London, NV, UK

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Springer-Verlag

Berlin, Heidelberg

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Published: 06 November 2006

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Brock HLaw FNakadai KNagashima Y(2020)Learning Three-dimensional Skeleton Data from Sign Language VideoACM Transactions on Intelligent Systems and Technology10.1145/337755211:3(1-24)Online publication date: 17-Apr-2020
https://dl.acm.org/doi/10.1145/3377552
Lim KTan ALee CTan S(2019)Isolated sign language recognition using Convolutional Neural Network hand modelling and Hand Energy ImageMultimedia Tools and Applications10.1007/s11042-019-7263-778:14(19917-19944)Online publication date: 2-Aug-2019
https://dl.acm.org/doi/10.1007/s11042-019-7263-7
Tsagaris ATrigkas D(2018)Mobile Gesture RecognitionProceedings of the International Conference on Machine Vision and Applications10.1145/3220511.3220519(13-17)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3220511.3220519
Anjo MPizzolato EFeuerstack SMaciel Cde Souza PAnacleto Jde Almeida Neris V(2012)A real-time system to recognize static gestures of Brazilian sign language (libras) alphabet using KinectProceedings of the 11th Brazilian Symposium on Human Factors in Computing Systems10.5555/2393536.2393574(259-268)Online publication date: 5-Nov-2012
https://dl.acm.org/doi/10.5555/2393536.2393574

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Real-time vision-based hand tracking and gesture recognition

Depth-based hand gesture recognition

A novel finger and hand pose estimation technique for real-time hand gesture recognition

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