Human distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people’s privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of \(\pm 0.2\) m in continuous-based estimation and \(96.8\%\) achieved-accuracy in discrete distance estimation.

人类距离估计在许多重要应用中至关重要，特别是在基于人类定位的系统中，例如老年人的独立生活应用，以及通过社交距离警报系统防止传染病传播来确保地方安全。以前估计参考传感设备和人类主体之间距离的方法依赖于视觉或高分辨率热像仪。然而，普通的视觉摄像机在室内环境中严重关注人们的隐私，而高分辨率热成像摄像机的成本很高。本文提出了一种使用低分辨率热传感器阵列估计以人为中心的室内应用距离的新方法。所提出的系统分别使用分类技术和人工神经网络提出了离散和自适应传感器放置连续距离估计器。它还通过一种新颖的基于图像的特征提出了一种基于距离的实时视野分类。此外，本文提出了对所提出的连续距离估计器的转移应用，以测量人体身高。所提出的方法在不同的室内环境、不同参与者的传感器放置中进行了评估。本文显示的中值总体误差为 本文提出了一种转移应用到所提出的连续距离估计器来测量人的身高。所提出的方法在不同的室内环境、不同参与者的传感器放置中进行了评估。本文显示的中值总体误差为 本文提出了一种转移应用到所提出的连续距离估计器来测量人的身高。所提出的方法在不同的室内环境、不同参与者的传感器放置中进行了评估。本文显示的中值总体误差为\(\pm 0.2\)  m 在基于连续的估计中和\(96.8\%\)在离散距离估计中实现的准确度。