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
Senior, A.: Tracking with Probabilistic Appearance Models. In: Proc. ECCV workshop on Performance Evaluation of Tracking and Surveillance Systems, pp. 48–55 (2002)
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)
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)
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)
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)
Choo, K., Fleet, D.J.: People tracking using hybrid Monte Carlo filtering. In: Proc. Int. Conf. Computer Vision, vol. II, pp. 321–328 (2001)
Anderson, B., Moore, J.: Optimal Filtering. Prentice-Hall, Englewood Cliffs (1979)
Kitagawa, G.: Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models. Journal of Computational and Graphical Statistics 5, 1–25 (1996)
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)
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)
Norlund, P., Eklundh, J.O.: Towards a Seeing Agent. In: Proc. of First Int. Workshop on Cooperative Distributed Vision, pp. 93–120 (1997)
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)
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)
Gardner, W.F., Lawton, D.T.: Interactive model based vehicle tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence 18, 1115–1121 (1996)
Swain, M.J., Ballard, D.H.: Color indexing. Int. J. of Computer Vision 7(1), 11–32 (1991)
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Jin, T., Lee, J. (2006). Robot Position Estimation and Tracking Using the Particle Filter and SOM in Robotic Space. In: Pan, Z., Cheok, A., Haller, M., Lau, R.W.H., Saito, H., Liang, R. (eds) Advances in Artificial Reality and Tele-Existence. ICAT 2006. Lecture Notes in Computer Science, vol 4282. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941354_54
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DOI: https://doi.org/10.1007/11941354_54
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