A Bayesian Approach to Object Identification

Hanna Pasula, University of California, Berkeley

There are many real world domains where an agent can observe the world state only partially and intermittently, using noisy sensors. Merely keeping track of the objects present in such a system is non-trivial. The problem may be complicated further if the system dynamics are not fully known or unpredictable, so that some on-line learning is necessary. Ihave been working on a principled approach to state estimation and prediction under these realistic conditions. So far, I have focused mostly on object identification, deciding if some newly observed object is the same as a previously observed one. The work has been applied to the surveillance of a large metropolitan freeway system.


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