Touch-Point Detection Using Thermal Video With Applications to Prevent Indirect Virus Spread.

Journal Article (Journal Article)

Viral and bacterial pathogens can be transmitted through direct contact with contaminated surfaces. Efficient decontamination of contaminated surfaces could lead to decreased disease transmission, if optimized methods for detecting contaminated surfaces can be developed. Here we describe such a method whereby thermal tracking technology is utilized to detect thermal signatures incurred by surfaces through direct contact. This is applicable in public places to assist with targeted sanitation and cleaning efforts to potentially reduce chance of disease transmission. In this study, we refer to the touched region of the surface as a "touch-point" and examine how the touch-point regions can be automatically localized with a computer vision pipeline of a thermal image sequence. The pipeline mainly comprises two components: a single-frame and a multi-frame analysis. The single-frame analysis consists of a Background subtraction method for image pre-processing and a U-net deep learning model for segmenting the touch-point regions. The multi-frame analysis performs a summation of the outputs from the single-frame analysis and creates a cumulative map of touch-points. Results show that the touch-point detection pipeline can achieve 75.0% precision and 81.5% F1-score for the testing experiments of predicting the touch-point regions. This preliminary study shows potential applications of preventing indirect pathogen spread in public spaces and improving the efficiency of cleaning sanitation.

Full Text

Duke Authors

Cited Authors

  • Ma, G; Ross, W; Tucker, M; Hsu, P-C; Buckland, DM; Codd, PJ

Published Date

  • January 2021

Published In

Volume / Issue

  • 9 /

Start / End Page

  • 4900711 -

PubMed ID

  • 34094721

Pubmed Central ID

  • PMC8172184

Electronic International Standard Serial Number (EISSN)

  • 2168-2372

International Standard Serial Number (ISSN)

  • 2168-2372

Digital Object Identifier (DOI)

  • 10.1109/jtehm.2021.3083098

Language

  • eng