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.

中文翻译：

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使用热视频进行接触点检测以及防止间接病毒传播的应用

病毒和细菌病原体可以通过直接接触受污染的表面传播。如果能够开发出检测受污染表面的优化方法，那么对受污染表面的有效净化可能会减少疾病传播。在这里，我们描述了一种利用热跟踪技术来检测表面通过直接接触产生的热特征的方法。这适用于公共场所，以协助有针对性的卫生和清洁工作，以潜在地减少疾病传播的机会。在本研究中，我们将表面的触摸区域称为“触摸点”，并研究如何使用热图像序列的计算机视觉管道自动定位触摸点区域。该流程主要包括两个部分：单帧分析和多帧分析。单帧分析由用于图像预处理的背景扣除方法和用于分割触摸点区域的 U-net 深度学习模型组成。多帧分析对单帧分析的输出进行求和，并创建触摸点的累积图。结果表明，在预测触摸点区域的测试实验中，触摸点检测管道可以实现 75.0% 的精度和 81.5% 的 F1 分数。这项初步研究显示了防止公共场所间接病原体传播和提高清洁卫生效率的潜在应用。