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The growing demand for intelligent environments unleashes an extraordinarycycle of privacy-aware applications that makes individuals' life morecomfortable and safe. Examples of these applications include pedestriantracking systems in large areas. Although the ubiquity of camera-based systems,they are not a preferable solution due to the vulnerability of leaking theprivacy of pedestrians. In this paper, we introduce a novel privacy-preservingsystem for pedestrian tracking in smart environments using multiple distributedLiDARs of non-overlapping views. The system is designed to leverage LiDARdevices to track pedestrians in partially covered areas due to practicalconstraints, e.g., occlusion or cost. Therefore, the system uses the pointcloud captured by different LiDARs to extract discriminative features that areused to train a metric learning model for pedestrian matching purposes. Toboost the system's robustness, we leverage a probabilistic approach to modeland adapt the dynamic mobility patterns of individuals and thus connect theirsub-trajectories. We deployed the system in a large-scale testbed with 70colorless LiDARs and conducted three different experiments. The evaluationresult at the entrance hall confirms the system's ability to accurately trackthe pedestrians with a 0.98 F-measure even with zero-covered areas. This resulthighlights the promise of the proposed system as the next generation ofprivacy-preserving tracking means in smart environments.

Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs