Privacy-Preserving Localization and Social Distance Monitoring with Low-Resolution Thermal Imaging and Deep Learning
Research output: Journal contributions › Journal articles › Research › peer-review
Standard
In: Procedia CIRP, Vol. 130, 12.2024, p. 355-361.
Research output: Journal contributions › Journal articles › Research › peer-review
Harvard
APA
Vancouver
Bibtex
}
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 - 2024/12
Y1 - 2024/12
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
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 -