Advertisement

A Vision Based Game Control Method

  • Peng Lu
  • Yufeng Chen
  • Xiangyong Zeng
  • Yangsheng Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3766)

Abstract

The appeal of computer games may be enhanced by vision-based user inputs. The high speed and low cost requirements for near-term, mass-market game applications make system design challenging. In this paper we propose a vision based 3D racing car game controlling method, which analyzes two fists positions of the player in video stream from the camera to get the direction commands of the racing car.

This paper especially focuses on the robust and real-time Bayesian network (BN) based multi-cue fusion fist tracking method. Firstly, a new strategy, which employs the latest work in face recognition, is used to create accurate color model of the fist automatically. Secondly, color cue and motion cue are used to generate the possible position of the fist. Then, the posterior probability of each possible position is evaluated by BN, which fuses color cue and appearance cue. Finally, the fist position is approximated by the hypothesis that maximizes a posterior. Based on the proposed control system, a racing car game, “Simulation Drive”, has been developed by our group. Through the game an entirely new experience can be obtained by the player.

Keywords

Bayesian Network Tracking Algorithm Shift Algorithm Propose Control System Game Control 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Freeman, W.T., Tanaka, K., Ohta, J.: Computer vision for computer games. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, pp. 100–105 (1996)Google Scholar
  2. 2.
    Akazawa, Y., Okada, Y.: Video Based Motion Capture System as Intuitive Interface for interactive 3D Games. In: International Journal of Intelligent Games and Simulation (IJIGS), vol. 2(2), pp. 64–71 (2003)Google Scholar
  3. 3.
    Maskell, S., Gordon, N.: A Tutorial on Particle Filters for On-line Nonlinear/Non-Gaussian Baysian Tracking. In: Proc. IEE Workshop Target Tracking: Algorithms and Applications (Oct. 2001)Google Scholar
  4. 4.
    Shan, C.F., Wei, Y.C., Tan, T.N.: Real Time Hand Tracking by Combining Particle Filtering and Mean Shift. In: The 6th International Conference on Automatic Face and Gesture Recognition, FG2004 (2004)Google Scholar
  5. 5.
    Comaniciu, D., Ramesh, V.: Mean Shift and Optimal Prediction for Efficient Object Tracking. In: ICIP, Vancouver, Canada, pp. 70–73 (2000)Google Scholar
  6. 6.
    Comaniciu, D., Meer, P.: Mean Shift Analysis and Applications. In: Proc. Seventh Int’l Conf. Computer Vision, pp. 1197–1203 (September 1999)Google Scholar
  7. 7.
    Lu, P., Huang, X.S., Wang, Y.S.: A New Framework for Handfree Navigation in 3D Game. In: Proceedings of the International Conference on CGIV 2004 (2004)Google Scholar
  8. 8.
    Cootes, T.F., Taylor, C.J.: Statistical Models of Appearance for computer vision. Imaging Science and Biomedical Engineering, University of Manchester, Manchester M13 9PT, U.K. (March 8, 2004)Google Scholar
  9. 9.
    Graf, H.P., Cosatto, E., Gibbon, D., Kocheisen, M., Petajan, E.: Multi-Modal System for Locating Heads and Faces, AFG, Killington, Vt, pp. 88–93 (1996)Google Scholar
  10. 10.
    Bradski, G.R.: Computer Vision Face Tracking For Use in a Perceptual User Interface. Intel Technology Journal 2 (1998)Google Scholar
  11. 11.
    Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997)CrossRefGoogle Scholar
  12. 12.
    Heckerman, D.: A Tutorial on Learning With Bayesian Networks, Microsoft Research Technical Report, MSR-TR-95-06Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Peng Lu
    • 1
  • Yufeng Chen
    • 1
  • Xiangyong Zeng
    • 1
  • Yangsheng Wang
    • 1
  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina

Personalised recommendations