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.

翻译：最近的多目标跟踪工作大多集中在中央系统上。与此形成对照的是,本文件展示了在分布式相机网络中实施的多目标跟踪方法。分布式系统的优点在于较轻的通信管理、对故障的强大性和当地决策。另一方面,数据协会和信息融合比集中式的组合更具挑战性,这主要是因为缺少全球完整的信息。拟议的算法在重新识别网络和分布式跟踪器管理模块的支持下,促进了分布式计算机过滤器的效益,以促进一致的信息。这些技术相互补充,并以简单有效的方式促进交叉摄像头数据协会。我们用已知的公开数据集评估整个系统,在不同条件下展示了将所有模块结合起来的优势。此外,我们将我们的算法与一些现有的集中式跟踪方法进行比较,在准确性和带宽使用方面表现优于它们的行为。