To date, the majority of positioning systems have been designed to operate within environments that have long-term stable macro-structure with potential small-scale dynamics. These assumptions allow the existing positioning systems to produce and utilize stable maps. However, in highly dynamic industrial settings these assumptions are no longer valid and the task of tracking people is more challenging due to the rapid large-scale changes in structure. In this paper we propose a novel positioning system for tracking people in highly dynamic industrial environments, such as construction sites. The proposed system leverages the existing CCTV camera infrastructure found in many industrial settings along with radio and inertial sensors within each worker's mobile phone to accurately track multiple people. This multi-target multi-sensor tracking framework also allows our system to use cross-modality training in order to deal with the environment dynamics. In particular, we show how our system uses cross-modality training in order to automatically keep track environmental changes (i.e. new walls) by utilizing occlusion maps. In addition, we show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy. We have conducted extensive real-world experiments in a construction site showing significant accuracy improvement via cross-modality training and the use of social forces.

翻译：迄今为止，大多数定位系统设计用于在具有长期稳定宏观结构及潜在小尺度动态的环境中运行。这些假设使得现有定位系统能够生成并利用稳定地图。然而，在高动态工业场景中，这些假设不再成立，由于结构快速大规模变化，人员追踪任务更具挑战性。本文提出了一种新型定位系统，用于在建筑工地等高动态工业环境中追踪人员。该系统充分利用工业场所常见的现有闭路电视摄像头基础设施，以及每位工人手机中的无线电和惯性传感器，实现多目标精确追踪。这一多目标多传感器追踪框架还允许系统通过跨模态训练应对环境动态。具体而言，我们展示了系统如何利用遮挡地图通过跨模态训练自动跟踪环境变化（例如新墙体）。此外，我们还展示了如何将这些地图与社会力模型结合，以精确预测人体运动并提高追踪精度。我们在建筑工地开展了大量真实场景实验，结果表明跨模态训练及社会力模型的应用显著提升了追踪精度。