IECON 2005 : the thirty-first Annual Conference of the IEEE Industrial Electronics Society : Sheraton Capital Center, Raleigh, North Carolina, USA : 6-10 November 2005

Probabilistic algorithms have been fully tested as the best solution in multiples areas, and thus in tracking tasks. Different solutions with them have been proposed for multiple objects tracking. The proposal of the authors is based on a particle filter whose robustness and adaptability is increased by the use of a clustering algorithm. Two different proposals for the segmentation process are presented in this paper, and interesting conclusions are extracted from their functional comparison. Tracking results are also presented in the paper, showing the reliability of the proposals.

[1]  Wolfram Burgard,et al.  Experiences with an Interactive Museum Tour-Guide Robot , 1999, Artif. Intell..

[2]  Dieter Fox,et al.  Bayesian Filtering for Location Estimation , 2003, IEEE Pervasive Comput..

[3]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[4]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[5]  Wolfram Burgard,et al.  Tracking multiple moving objects with a mobile robot , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  H. Durrant-Whyte,et al.  Mobile vehicle navigation in unknown environments: a multiple hypothesis approach , 1995 .

[7]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Esther Koller-Meier,et al.  Tracking multiple objects using the Condensation algorithm , 2001, Robotics Auton. Syst..

[9]  William Fitzgerald,et al.  A Bayesian approach to tracking multiple targets using sensor arrays and particle filters , 2002, IEEE Trans. Signal Process..

[10]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[11]  Sebastian Thrun,et al.  Probabilistic Algorithms in Robotics , 2000, AI Mag..

[12]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[13]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.