Moving Object Tracking Using the Particle Filter and SOM in Robotic Space with Network Sensors

Position estimation is one of the most important functions for the mobile robot navigating in Robotic Space. 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(Self Organizing Map) 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.

[1]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Luc Van Gool,et al.  Object Tracking with an Adaptive Color-Based Particle Filter , 2002, DAGM-Symposium.

[3]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[4]  Kuu-Young Young,et al.  An intelligent radar predictor for high-speed moving-target tracking , 2002, 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM '02. Proceedings..

[5]  M. Bierlaire,et al.  Behavioral Dynamics for Pedestrians , 2003 .

[6]  Jonathan M. Roberts,et al.  Improved Kalman filter initialisation using neurofuzzy estimation , 1995 .

[7]  David J. Fleet,et al.  People tracking using hybrid Monte Carlo filtering , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Daryl T. Lawton,et al.  Interactive Model-Based Vehicle Tracking , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  N. Atsushi,et al.  Tracking multiple people using distributed vision systems , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[10]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[11]  S. Shankar Sastry,et al.  Vision guided navigation for a nonholonomic mobile robot , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[12]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  Peter K. Allen,et al.  Trajectory filtering and prediction for automated tracking and grasping of a moving object , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.