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HMM-Based Gesture Recognition for Robot Control

  • Hye Sun Park
  • Eun Yi Kim
  • Sang Su Jang
  • Se Hyun Park
  • Min Ho Park
  • Hang Joon Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3522)

Abstract

In this paper, we present a gesture recognition system for an interaction between a human being and a robot. To recognize human gesture, we use a hidden Markov model (HMM) which takes a continuous stream as an input and can automatically segments and recognizes human gestures. The proposed system is composed of three modules: a pose extractor, a gesture recognizer, and a robot controller. The pose extractor replaces an input frame by a pose symbol. In this system, a pose represents the position of user’s face and hands. Thereafter the gesture recognizer recognizes a gesture using a HMM, which performs both segmentation and recognition of the human gesture simultaneously [6]. Finally, the robot controller handles the robot as transforming the recognized gesture into robot commands. To assess the validity of the proposed system, we used the proposed recognition system as an interface to control robots, RCB-1 robot. The experimental results verify the feasibility and validity of the proposed system.

Keywords

Hide Markov Model Gesture Recognition Robot Control Robot Controller Input Frame 
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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References

  1. 1.
    Hu, C., Meng, M.Q., Liu, P.X., Wang, X.: Visual Gesture Recognition for Human-Machine Interface of Robot Teleoperation. IEEE/RSJ, 1560–1565 (2003)Google Scholar
  2. 2.
    Moy, M.C.: Gesture-based Interaction with a Pet Robot. In: AAAI, pp. 628–633 (1999)Google Scholar
  3. 3.
    Fong, T., et al.: Novel interfaces for remote driving:gesture, haptic and PDA. In: SPIE Telemanipulator and Telepresence Technolgies VII (2000)Google Scholar
  4. 4.
    Oka, K., Sato, Y., Koike, H.: Real-time tracking of multiple fingertips and gesture recognition for augmented desk interface systems. In: FGR, pp. 411–416 (2002)Google Scholar
  5. 5.
    Corradini, A., Gross, H.-M.: Camera-based gesture recognition for robot control. In: IJCNN (2000)Google Scholar
  6. 6.
    Park, H.S., Kim, E.Y., Kim, H.J.: A Hidden Markov Model for Gesture Recognition. Pattern Recognition (in review)Google Scholar
  7. 7.
    Yang, J., Waibel, A.: A real-time face tracker. WACV 15(1), 142–147 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hye Sun Park
    • 1
  • Eun Yi Kim
    • 2
  • Sang Su Jang
    • 1
  • Se Hyun Park
    • 3
  • Min Ho Park
    • 4
  • Hang Joon Kim
    • 1
  1. 1.Department of Computer EngineeringKyungpook National UnivKorea
  2. 2.Department of Internet and Multimedia EngineeringKonkuk Univ., NITRIKorea
  3. 3.School of Computer and CommunicationDaegu UnivKorea
  4. 4.Information Technology ServicesKyungpook National UnivKorea

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