Robot Position Estimation and Tracking Using the Particle Filter and SOM in Robotic Space

  • TaeSeok Jin
  • JangMyung Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4282)

Abstract

The Robotic Space is the space where many intelligent sensing and tracking devices, such as computers and multi sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human following by fusing distributed multiple vision systems in Robotic Space, with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into SOM based particle filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving objects by generating hypotheses not in the image plan but on the top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multi-motion tracking. Simulations are carried out to evaluate the proposed performance. Also, the method is applied to the intelligent environment and its performance is verified by the experiments.

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References

  1. 1.
    Senior, A.: Tracking with Probabilistic Appearance Models. In: Proc. ECCV workshop on Performance Evaluation of Tracking and Surveillance Systems, pp. 48–55 (2002)Google Scholar
  2. 2.
    Bierlaire, M., Antonini, G., Weber, M.: Behavioural Dynamics for Pedestrians. In: Axhausen, K. (ed.) Moving through nets: the physical and social dimensions of travel, pp. 1–18. Elsevier, Amsterdam (2003)Google Scholar
  3. 3.
    Nummiaro, K., Koller-Meier, E., Van Gool, L.J.: Object Tracking with an Adaptive Color- Based Particle Filter. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 353–360. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Allen, P.K., Tmcenko, A., Yoshimi, B., Michelman, P.: Trajectory filtering and prediction for automated tracking and grasping of a moving object. In: IEEE International Conference on Robotics and Automation, pp. 1850–1856 (1992)Google Scholar
  5. 5.
    Ma, Y., Kosecka, J., Sastry, S.S.: Vision guided navigation for a nonholonomic mobile robot. IEEE Transaction on Robotics and Automation 15(3), 521–536 (1999)CrossRefGoogle Scholar
  6. 6.
    Choo, K., Fleet, D.J.: People tracking using hybrid Monte Carlo filtering. In: Proc. Int. Conf. Computer Vision, vol. II, pp. 321–328 (2001)Google Scholar
  7. 7.
    Anderson, B., Moore, J.: Optimal Filtering. Prentice-Hall, Englewood Cliffs (1979)MATHGoogle Scholar
  8. 8.
    Kitagawa, G.: Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models. Journal of Computational and Graphical Statistics 5, 1–25 (1996)MathSciNetGoogle Scholar
  9. 9.
    Chen, Y.-Y., Young, K.-y.: An intelligent radar predictor for high-speed moving- target tracking. In: TENCON 2002. Proceedings. IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, vol. 3, pp. 1638–1641 (2002)Google Scholar
  10. 10.
    Roberts, J.M., Mills, D.J., Charnley, D., Harris, C.J.: Improved Kalman filter initialization using neuro-fuzzy estimation. In: Int’l. Conf. on Artificial Neural Networks, pp. 329–334 (1995)Google Scholar
  11. 11.
    Norlund, P., Eklundh, J.O.: Towards a Seeing Agent. In: Proc. of First Int. Workshop on Cooperative Distributed Vision, pp. 93–120 (1997)Google Scholar
  12. 12.
    Atsushi, N., Hirokazu, K., Shinsaku, H., Seiji, I.: Tracking Multiple People using Distributed Vision Systems. In: Proc. of the 2002 IEEE Int. Conf. on Robotics & Automation, pp. 2974–2981 (2002)Google Scholar
  13. 13.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-Time Tracking of the Human Body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 780–785 (1997)CrossRefGoogle Scholar
  14. 14.
    Gardner, W.F., Lawton, D.T.: Interactive model based vehicle tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence 18, 1115–1121 (1996)CrossRefGoogle Scholar
  15. 15.
    Swain, M.J., Ballard, D.H.: Color indexing. Int. J. of Computer Vision 7(1), 11–32 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • TaeSeok Jin
    • 1
  • JangMyung Lee
    • 2
  1. 1.Dept. of Mechatronics EngineeringDongSeo UniversityBusanKorea
  2. 2.Dept. of Elctronics EngineeringPusan National UniversityBusanKorea

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