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.
@article{arxiv.2105.10935,
title = {Principled information fusion for multi-view multi-agent surveillance systems},
author = {Bailu Wang and Suqi Li and Giorgio Battistelli and Luigi Chisci and Wei Yi},
journal= {arXiv preprint arXiv:2105.10935},
year = {2021}
}