In this study, the authors discuss a novel approach for multi-camera-based unobtrusive identification and tracking of occupants in wide-area, multi-building scenarios. Considering the scalability issues in adopting a centralised approach to monitor wide-area scenarios, they proposed a distributed approach to occupant identification and tracking. The key technical idea underlying their approach is to abstract a wide-area indoor surveillance environment using a distributed state transition system (DSTS) model, which in turn is composed of independent building-specific state transition systems, coordinating and collaborating with each other. This study presents the details of their DSTS model and examines the temporal ordering of recognition events within the DSTS for ensuring accurate state information and responses to spatio–temporal queries. They also provide an experimental evaluation of the performance of their model using precision-recall metrics. Their conclusion is that the DSTS model serves as an efficient mechanism for tracking occupants in wide-area, multi-building scenarios monitored by camera networks.

中文翻译：

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为大型摄像机网络建模以进行识别和跟踪：一个抽象框架

在这项研究中，作者讨论了一种新颖的方法，可以在广域，多建筑物的场景中基于多摄像机的识别和跟踪乘员。考虑到采用集中式方法来监视广域场景时的可伸缩性问题，他们提出了一种用于识别和跟踪乘员的分布式方法。他们的方法所基于的关键技术思想是使用分布式状态转换系统（DSTS）模型来抽象一个广域的室内监视环境，该模型又由独立的特定于建筑物的状态转换系统组成，相互协调和协作。这项研究提出了他们的DSTS模型的细节，并检查了DSTS中识别事件的时间顺序，以确保准确的状态信息和对时空查询的响应。他们还使用精确召回指标对模型的性能进行实验评估。他们的结论是，DSTS模型可作为一种有效的机制，用于跟踪由摄像机网络监控的广域，多建筑物场景中的居住者。