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

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Authors

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.
Original languageEnglish
JournalProcedia CIRP
Volume130
Pages (from-to)355-361
Number of pages7
ISSN2212-8271
DOIs
Publication statusPublished - 01.01.2024
Event57th CIRP Conference on Manufacturing Systems - CIRP CMS 2024: Speeding up manufacturing - Universität Minho, Póvoa de Varzim , Portugal
Duration: 29.05.202431.05.2024
Conference number: 57
https://www.cirpcms2024.org/

Bibliographical note

Publisher Copyright:
© 2024 The Authors.

    Research areas

  • Deep Learning, Infrared Sensors, Convolutional Neural Networks, Facility Layout Planning, Multiple Object Localization
  • Engineering

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