Encoding context likelihood functions as classifiers in particle filters for target tracking

In this work we address the problem of multilevel context representation and exploitation for target tracking. Specifically, we present an approach for encoding different types of contextual information (CI) as likelihood functions via classifiers in particle filters. The proposed solution is sufficiently versatile as to be able to couch different types of CI. Promising results have been obtained from our simulations on synthetic data