  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.
