Hierarchical Bayesian Modeling for Estimating Shared Hidden States with Application to Tracking
Kazuhiko Kawamoto · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2009
We consider a problem of estimating shared hidden states of stochastic time–series models from individual observations. To solve the problem, we formulate the problem with hierarchical Bayesian Models and propose a particle filter, which yields a numerical solution to sequential Bayesian estimation, for the hierarchical models. The proposed method can be applied to the problem of tracking an extended object or a group of objects moving in formation. We decompose the state space into the shared and unshared substates, assuming that the individual unshared substate independently evolves with time. This assumption enables us to treat multiple targets individually. Experiments with numerical and real image sequences show the effectiveness of the proposed method.