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CIRA
2007
IEEE

Learning Tactic-Based Motion Models of a Moving Object with Particle Filtering

12 years 2 months ago
Learning Tactic-Based Motion Models of a Moving Object with Particle Filtering
— Learning motion models of a moving object is a challenge for autonomous robots. We address the particular instance of parameter learning when tracking object motions in a switching multi-model system. We present a general algorithm of joint parameter-state estimation based on multimodel particle filter. We apply the approach to a specific balltracking problem and extend the algorithm to learn model parameters in a dynamic Bayesian network (DBN). We show empirical results in simulation and in a team robot soccer environment, as a substrate for applying the learned models to object tracking in a team. The learning capability allow the tracker to much more effectively track mobile objects.
Yang Gu, Manuela M. Veloso
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where CIRA
Authors Yang Gu, Manuela M. Veloso
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