  A key objective of multi-agent surveillance systems is to monitor a much
larger region than the limited field-of-view (FoV) of any individual agent by
successfully exploiting cooperation among multi-view agents. Whenever either a
centralized or a distributed approach is pursued, this goal cannot be achieved
unless an appropriately designed fusion strategy is adopted. This paper
presents a novel principled information fusion approach for dealing with
multi-view multi-agent case, on the basis of Generalized Covariance
Intersection (GCI). The proposed method can be used to perform multi-object
tracking on both a centralized and a distributed peer-to-peer sensor network.
Simulation experiments on realistic multi-object tracking scenarios demonstrate
effectiveness of the proposed solution.
