# Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs

arXiv: https://arxiv.org/abs/2303.09915 · HTML: https://mlsanity.com/paper/2303.09915/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID](https://mlsanity.com/paper/2006.02713/) (2020)
- [Learning Fine-grained Image Similarity with Deep Ranking](https://mlsanity.com/paper/1404.4661/) (2014)
- [MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking](https://mlsanity.com/paper/2010.07548/) (2020)
- [CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification](https://mlsanity.com/paper/1903.09254/) (2019)
- [RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment](https://mlsanity.com/paper/1910.05839/) (2019)
- [Features for Multi-Target Multi-Camera Tracking and Re-Identification](https://mlsanity.com/paper/1803.10859/) (2018)
- [Joint Monocular 3D Vehicle Detection and Tracking](https://mlsanity.com/paper/1811.10742/) (2018)
- [JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset](https://mlsanity.com/paper/2002.08397/) (2020)
- [Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds](https://mlsanity.com/paper/1904.07537/) (2019)
- [End-to-end Learning of Multi-sensor 3D Tracking by Detection](https://mlsanity.com/paper/1806.11534/) (2018)
- [Robust Depth-based Person Re-identification](https://mlsanity.com/paper/1703.09474/) (2017)
- [No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs](https://mlsanity.com/paper/1802.08755/) (2018)
- [3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks](https://mlsanity.com/paper/1711.08241/) (2017)

## Built on by (most cited first, 3 of 3)

- [Privacy-Preserving Taxi-Demand Prediction Using Federated Learning](https://mlsanity.com/paper/2305.08107/) (2023)
- [Privacy-Preserving by Design: Indoor Positioning System Using Wi-Fi Passive TDOA](https://mlsanity.com/paper/2306.02211/) (2023)
- [TimeSense: Multi-Person Device-free Indoor Localization via RTT](https://mlsanity.com/paper/2409.00030/) (2024)

## Abstract (arXiv)

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.