Modeling for Gesture Set Design toward Realizing Effective Human-Vehicle Interface

  • Cheoljong Yang
  • Jongsung Yoon
  • Jounghoon Beh
  • Hanseok Ko
Part of the Studies in Computational Intelligence book series (SCI, volume 365)


Intuitive driver-to-vehicle interface is highly desirable as we experience rapid increase of vehicle device complexity in modern day automobile. This paper addresses the gesture mode of interface and proposes an effective gesture language set capable of providing automotive control via hand gesture as natural but safe human-vehicle interface. Gesture language set is designed based on practical motions of single hand gesture. Proposed language set is optimized for in-vehicle imaging environment. Feature mapping for recognition is achieved using hidden Markov model which effectively captures the hand motion descriptors. Representative experimental results indicate that the recognition performance of proposed language set is over 99%, which makes it promising for real vehicle application.


language set gesture recognition driver-vehicle interface HMM 


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  1. 1.
    McCall, J., Trivedi, M.: Video-Based Lane Estimation and Tracking for Driver Assistance: Survey. System, and Evaluation. IEEE trans. intelligent transportation systems 7(1), 20–37 (2006)CrossRefGoogle Scholar
  2. 2.
    Gavrila, D.: Sensor-based pedestrian protection. IEEE intelligent systems 16, 77–81 (2001)CrossRefGoogle Scholar
  3. 3.
    Handmann, U.: An image processing system for driver assistance. Image and Vision Computing 18, 367–376 (2000)CrossRefGoogle Scholar
  4. 4.
    Zu, J., et al.: Vison-guided automatic parking for smart car. IEEE intelligent Vehicles symposium, 725–730 (2000)Google Scholar
  5. 5.
    Alpern, M., Minardo, K.: Developing a car gesture interface for use as a secondary task. In: CHI 2003 extended abstracts on Human factors in computing systems, pp. 932–993 (2003)Google Scholar
  6. 6.
    Tonnis, M., et al.: Experimental Evaluation of an Augmented Reality Vsualization for Direction a Car Driver’s Attention. In: International Symposium on Mixed and Augmented Reality (2005)Google Scholar
  7. 7.
    Lee, H., Kim, J.: An HMM-Based Threshold Model Approach for Gesture Recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(10), 961–973 (1999)CrossRefGoogle Scholar
  8. 8.
    Liang, R., Ouhyoung, M.: A Real-Time Continuous Gesture Recognition System for Sign Language. In: Proceedings of the Third Int. Conference on Automatic Face and Gesture Recognition, Nara (Japan), pp. 558–565 (1998)Google Scholar
  9. 9.
    Elmezain, M., et al.: A Hidden Markov Model-based continuous gesture recognition system for hand motion trajectory. In: International Conference on Pattern Recognition, pp. 1–4 (2008)Google Scholar
  10. 10.
    Vogler, C., Metaxas, D.: Parallel hidden Markov models for American Sign Language recognition. In: Proc. Seventh International Conference on Computer Vision, vol. 1, pp. 116–122 (1999)Google Scholar
  11. 11.
    Shon, S., Beh, J., Wang, H.: Robot User Control System using Hand Gesture Recognizer. Journal of Institute of Control, Robotics and Systems 17(4) ( in press 2011)Google Scholar
  12. 12.
    Zhenyao, M., Neumann, U.: Real-time hand pose recognition using low-resolution depth images. In: IEEE Computer Vision and Pattern Recognition, pp. 1499–1505 (2006)Google Scholar
  13. 13.
    Dreuw, P., et al.: Speech Recognition Techniques for a Sign Language Recognition System. Interspeech, 705–708 (2007)Google Scholar
  14. 14.
    Neitzel, L., et al.: A reviews of crane safety in the construction industry. Applied Occupational and Environmental Hygiene 16(12), 1106–1117 (2001)CrossRefGoogle Scholar
  15. 15.
    Shon, S., Beh, J., Wang, H., Yang, C., Ko, H.: Hand Motion Design for Performance Enhancement of Vision Based Hand Signal Recognizer. Journal of IEEK, SP 48 (in press 2011)Google Scholar
  16. 16.
    Pentland, A., Liu, A.: Modeling and prediction of human behavior. Neural Computation 11, 229–242 (1999)CrossRefGoogle Scholar
  17. 17.
    Chai, D., Bouzerdoum, A.: A Bayesian approach to skin color classification in YCbCr color space. In: Proceedings of TENCON, vol. 2, pp. 421–424 (2000)Google Scholar
  18. 18.
    Liu, N., et al.: Model Structure Selection and Training Algorithms for an HMM Gesture Recognition System. In: International Workshop on Frontiers in Handwriting Recognition, pp. 100–105 (2004)Google Scholar
  19. 19.
    Baum, L.: An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes. Inequalities 3(9), 1–8 (1972)Google Scholar
  20. 20.
    Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–285 (1989)CrossRefGoogle Scholar
  21. 21.
    Young, S., et al.: The HTK Book (for HTK version 3.4) (2009),

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cheoljong Yang
    • 1
  • Jongsung Yoon
    • 1
  • Jounghoon Beh
    • 2
  • Hanseok Ko
    • 1
  1. 1.Vision Information ProcessingKorea UniversitySeoulKorea
  2. 2.Institute for Advanced Computer StudiesUniversity of MarylandCollege parkUSA

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