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：智能环境日益增长的需求催生了非凡的隐私感知应用循环，使个人生活更加舒适安全。此类应用包括大范围区域的行人追踪系统。尽管基于摄像头的系统无处不在，但由于存在泄露行人隐私的脆弱性，它们并非优选方案。本文提出了一种新颖的隐私保护系统，利用多个非重叠视角的分布式激光雷达在智能环境中进行行人追踪。该系统旨在利用激光雷达设备对因实际限制（如遮挡或成本）而部分覆盖的区域进行行人追踪。为此，系统利用不同激光雷达捕获的点云提取具有判别力的特征，用于训练度量学习模型以实现行人匹配。为增强系统的鲁棒性，我们采用概率方法对个体的动态移动模式进行建模与自适应，从而连接其子轨迹。我们在包含70个无色激光雷达的大规模测试平台上部署该系统，并开展了三项不同实验。入口大厅的评估结果表明，即使在零覆盖区域，该系统也能以0.98的F值准确追踪行人。这一结果凸显了所提系统作为智能环境中下一代隐私保护追踪手段的潜力。