The growing demand for intelligent environments unleashes an extraordinary cycle of privacy-aware applications that makes individuals' life more comfortable and safe. Examples of these applications include pedestrian tracking systems in large areas. Although the ubiquity of camera-based systems, they are not a preferable solution due to the vulnerability of leaking the privacy of pedestrians.In this paper, we introduce a novel privacy-preserving system for pedestrian tracking in smart environments using multiple distributed LiDARs of non-overlapping views. The system is designed to leverage LiDAR devices to track pedestrians in partially covered areas due to practical constraints, e.g., occlusion or cost. Therefore, the system uses the point cloud captured by different LiDARs to extract discriminative features that are used to train a metric learning model for pedestrian matching purposes. To boost the system's robustness, we leverage a probabilistic approach to model and adapt the dynamic mobility patterns of individuals and thus connect their sub-trajectories.We deployed the system in a large-scale testbed with 70 colorless LiDARs and conducted three different experiments. The evaluation result at the entrance hall confirms the system's ability to accurately track the pedestrians with a 0.98 F-measure even with zero-covered areas. This result highlights the promise of the proposed system as the next generation of privacy-preserving tracking means in smart environments.

翻译：随着对智能环境的需求增长，释放了一个使个人生活更舒适和安全的隐私感知式应用程序的非凡周期。这些应用程序的示例包括大面积的行人跟踪系统。尽管相机型系统的普及，但由于泄露行人隐私的脆弱性，它们不是首选的解决方案。在本文中，我们介绍了一种新的面向智能环境的行人跟踪隐私保护系统，使用多个分布式的3D LiDAR进行不重叠的视图。该系统旨在利用LiDAR设备来跟踪由于实际约束（例如遮挡或成本）而处于部分覆盖区域中的行人。因此，系统利用不同LiDAR捕获的点云来提取判别性特征，这些特征用于训练度量学习模型以进行行人匹配。为了增强系统的鲁棒性，我们采用概率方法来建模和适应个体的动态移动模式，从而连接它们的子轨迹。我们在一个大型测试平台上部署了该系统，使用了70个无色LiDAR，并进行了三个不同的实验。入口大厅的评估结果确认了该系统即使在零覆盖区域也能够准确地跟踪行人，其F1度量值为0.98。这个结果强调了该系统作为未来隐私保护跟踪手段的潜力。