  This article addresses the problem of multi-object tracking by using a
non-deterministic model of target behaviors with hard constraints. To capture
the evolution of target features as well as their locations, we permit objects
to lie in a general topological target configuration space, rather than a
Euclidean space. We obtain tracker performance bounds based on sample rates,
and derive a flexible, agnostic tracking algorithm. We demonstrate our
algorithm on two scenarios involving laboratory and field data.
