Privacy-Preserving Localization and Social Distance Monitoring with Low-Resolution Thermal Imaging and Deep Learning

Andrei Perov; Jens Heger

doi:10.1016/j.procir.2024.10.100

Privacy-Preserving Localization and Social Distance Monitoring with Low-Resolution Thermal Imaging and Deep Learning

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

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Privacy-Preserving Localization and Social Distance Monitoring with Low-Resolution Thermal Imaging and Deep Learning. / Perov, Andrei; Heger, Jens.
in: Procedia CIRP, Jahrgang 130, 01.01.2024, S. 355-361.

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Bibtex

@article{7a2f824ad6964aae8eafa8e345cd0acb,

title = "Privacy-Preserving Localization and Social Distance Monitoring with Low-Resolution Thermal Imaging and Deep Learning",

abstract = "This study introduces a novel approach to leverage low-power, low-resolution infrared sensors for detailed people tracking in manufacturing settings. We curated a dataset including a diverse range of interactions labeled for multiple-person localization and social distance violation tasks. Our methodology uses a combination of convolutional and recurrent neural networks to interpret spatiotemporal data. We demonstrate the capability of the novel image segmentation approach for human localization where we achieve 97.5 percent image-level accuracy. Also, we highlight the importance of interpolation and convolutional kernel selection for social distance tasks where we achieve 91 percent macro-averaged accuracy in 4 class scenarios.",

keywords = "Deep Learning, Infrared Sensors, Convolutional Neural Networks, Facility Layout Planning, Multiple Object Localization, Engineering",

author = "Andrei Perov and Jens Heger",

note = "Publisher Copyright: {\textcopyright} 2024 The Authors.; 57th CIRP Conference on Manufacturing Systems - CIRP CMS 2024 : Speeding up manufacturing, CIRP CMS '24 ; Conference date: 29-05-2024 Through 31-05-2024",

year = "2024",

month = jan,

day = "1",

doi = "10.1016/j.procir.2024.10.100",

language = "English",

volume = "130",

pages = "355--361",

journal = "Procedia CIRP",

issn = "2212-8271",

publisher = "Elsevier B.V.",

url = "https://www.cirpcms2024.org/",

}

RIS

TY - JOUR

T1 - Privacy-Preserving Localization and Social Distance Monitoring with Low-Resolution Thermal Imaging and Deep Learning

AU - Perov, Andrei

AU - Heger, Jens

N1 - Conference code: 57

PY - 2024/1/1

Y1 - 2024/1/1

N2 - This study introduces a novel approach to leverage low-power, low-resolution infrared sensors for detailed people tracking in manufacturing settings. We curated a dataset including a diverse range of interactions labeled for multiple-person localization and social distance violation tasks. Our methodology uses a combination of convolutional and recurrent neural networks to interpret spatiotemporal data. We demonstrate the capability of the novel image segmentation approach for human localization where we achieve 97.5 percent image-level accuracy. Also, we highlight the importance of interpolation and convolutional kernel selection for social distance tasks where we achieve 91 percent macro-averaged accuracy in 4 class scenarios.

AB - This study introduces a novel approach to leverage low-power, low-resolution infrared sensors for detailed people tracking in manufacturing settings. We curated a dataset including a diverse range of interactions labeled for multiple-person localization and social distance violation tasks. Our methodology uses a combination of convolutional and recurrent neural networks to interpret spatiotemporal data. We demonstrate the capability of the novel image segmentation approach for human localization where we achieve 97.5 percent image-level accuracy. Also, we highlight the importance of interpolation and convolutional kernel selection for social distance tasks where we achieve 91 percent macro-averaged accuracy in 4 class scenarios.

KW - Deep Learning

KW - Infrared Sensors

KW - Convolutional Neural Networks

KW - Facility Layout Planning

KW - Multiple Object Localization

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85213010738&partnerID=8YFLogxK

U2 - 10.1016/j.procir.2024.10.100

DO - 10.1016/j.procir.2024.10.100

M3 - Journal articles

VL - 130

SP - 355

EP - 361

JO - Procedia CIRP

JF - Procedia CIRP

SN - 2212-8271

T2 - 57th CIRP Conference on Manufacturing Systems - CIRP CMS 2024

Y2 - 29 May 2024 through 31 May 2024

ER -