  Most recent works on multi-target tracking with multiple cameras focus on
centralized systems. In contrast, this paper presents a multi-target tracking
approach implemented in a distributed camera network. The advantages of
distributed systems lie in lighter communication management, greater robustness
to failures and local decision making. On the other hand, data association and
information fusion are more challenging than in a centralized setup, mostly due
to the lack of global and complete information. The proposed algorithm boosts
the benefits of the Distributed-Consensus Kalman Filter with the support of a
re-identification network and a distributed tracker manager module to
facilitate consistent information. These techniques complement each other and
facilitate the cross-camera data association in a simple and effective manner.
We evaluate the whole system with known public data sets under different
conditions demonstrating the advantages of combining all the modules. In
addition, we compare our algorithm to some existing centralized tracking
methods, outperforming their behavior in terms of accuracy and bandwidth usage.
