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.

本研究提出了一种基于 LiDAR 的隐私保护系统，用于智能环境下的行人追踪，利用多个分布式 LiDAR 的非重叠视图跟踪部分被遮挡的行人，并使用概率方法来建模和调整个体的动态运动模式，结果表明该系统能够准确地跟踪行人，是下一代智能环境中的有前途的保护隐私的跟踪手段。