MilliTRACE-IR: Contact Tracing and Temperature Screening via mm-Wave and
Infrared Sensing
- URL: http://arxiv.org/abs/2110.03979v1
- Date: Fri, 8 Oct 2021 08:58:36 GMT
- Title: MilliTRACE-IR: Contact Tracing and Temperature Screening via mm-Wave and
Infrared Sensing
- Authors: Marco Canil, Jacopo Pegoraro, Michele Rossi
- Abstract summary: milliTRACE-IR is a joint mm-wave radar and infrared imaging sensing system.
The system achieves fully automated measurement of distancing and body temperature.
A person with high body temperature is reliably detected by the thermal camera sensor and subsequently traced across a large indoor area in a non-invasive way by the radars.
- Score: 4.6838063911731025
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work, we present milliTRACE-IR, a joint mm-wave radar and infrared
imaging sensing system performing unobtrusive and privacy preserving human body
temperature screening and contact tracing in indoor spaces. Social distancing
and fever detection have been widely employed to counteract the COVID-19
pandemic, sparking great interest from academia, industry and public
administrations worldwide. While most solutions have dealt with the two aspects
separately, milliTRACE-IR combines, via a robust sensor fusion approach,
mm-wave radars and infrared thermal cameras. The system achieves fully
automated measurement of distancing and body temperature, by jointly tracking
the faces of the subjects in the thermal camera image plane and the human
motion in the radar reference system. It achieves decimeter-level accuracy in
distance estimation, inter-personal distance estimation (effective for subjects
getting as close as 0.2 m), and accurate temperature monitoring (max. errors of
0.5 C). Moreover, milliTRACE-IR performs contact tracing: a person with high
body temperature is reliably detected by the thermal camera sensor and
subsequently traced across a large indoor area in a non-invasive way by the
radars. When entering a new room, this subject is re-identified among several
other individuals with high accuracy (95%), by computing gait-related features
from the radar reflections through a deep neural network and using a weighted
extreme learning machine as the final re-identification tool.
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