Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs

Ohno, Masakazu; Ukyo, Riki; Amano, Tatsuya; Rizk, Hamada; Yamaguchi, Hirozumi

doi:https://doi.org/10.1109/PERCOM56429.2023.10099061

Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs

Ohno, Masakazu; Ukyo, Riki; Amano, Tatsuya et al.

2023 IEEE International Conference on Pervasive Computing and Communications, PerCom 2023, 2023, 43-52

Number of Access:515（2025-08-14 07:15 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/100956

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Title	Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs
Creator	CreatorName Ohno, Masakazu
	CreatorName Ukyo, Riki
	NRID 1000040906898 1000040906898 ORCID 0000-0002-8011-247X 0000-0002-8011-247X researchmap permalink tatsuya-amano tatsuya-amano Scopus Author ID 57192709613 57192709613 CreatorName Amano, Tatsuya
	NRID 1000030890280 1000030890280 ORCID 0000-0002-8278-8801 0000-0002-8278-8801 researchmap permalink rizk rizk CreatorName Rizk, Hamada
	NRID 1000080314409 1000080314409 ORCID 0000-0003-2273-4876 0000-0003-2273-4876 researchmap permalink hirozumi.yamaguchi hirozumi.yamaguchi Scopus Author ID 7404274201 7404274201 CreatorName Yamaguchi, Hirozumi
Subject	LiDAR
	Pedestrian tracking
	Point cloud-based recognition
	Privacy-preserving
	Re-ID
Description	Ohno M., Ukyo R., Amano T., et al. Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs. 2023 IEEE International Conference on Pervasive Computing and Communications, PerCom 2023 , 43 (2023); https://doi.org/10.1109/PERCOM56429.2023.10099061.
Abstract	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.
Publisher	Institute of Electrical and Electronics Engineers Inc.
Source Title	2023 IEEE International Conference on Pervasive Computing and Communications, PerCom 2023
Page	43-52
Date of Issued	2023
Language	English
Handle URL	https://hdl.handle.net/11094/100956
Relation.isVersionOf	DOI (Publisher Version) https://doi.org/10.1109/PERCOM56429.2023.10099061
Access Rights	open access
CopyRight	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
oaire:version	Accepted Manuscript
Category	会議発表論文 Conference Paper

Resource Type	conference paper
Local Type	Conference Paper
DCTERMS.bibliographicCitation	2023 IEEE International Conference on Pervasive Computing and Communications, PerCom 2023. 2023, p. 43-52
DC.title	Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs
DC.creator	Ohno, Masakazu
	Ukyo, Riki
	Amano, Tatsuya
	Rizk, Hamada
	Yamaguchi, Hirozumi
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	English
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2023
DC.identifier	https://doi.org/10.1109/PERCOM56429.2023.10099061
DC.subject	LiDAR
	Pedestrian tracking
	Point cloud-based recognition
	Privacy-preserving
	Re-ID
DC.description	Ohno M., Ukyo R., Amano T., et al. Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs. 2023 IEEE International Conference on Pervasive Computing and Communications, PerCom 2023 , 43 (2023); https://doi.org/10.1109/PERCOM56429.2023.10099061.
DCTERMS.abstract	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.
DC.rights	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DC.format	100956
citation_title	Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs
citation_author	Ohno, Masakazu
	Ukyo, Riki
	Amano, Tatsuya
	Rizk, Hamada
	Yamaguchi, Hirozumi
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	English
citation_date	2023
citation_journal_title	2023 IEEE International Conference on Pervasive Computing and Communications, PerCom 2023
citation_firstpage	43
citation_lastpage	52
citation_doi	https://doi.org/10.1109/PERCOM56429.2023.10099061
citation_public_url	https://hdl.handle.net/11094/100956

Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs

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