Towards human distance estimation using a thermal sensor array

• Published in: Neural computing & applications Vol. 35; no. 32; pp. 23357 - 23367
• Main Authors: Naser, Abdallah, Lotfi, Ahmad, Zhong, Joni
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2023; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Cameras; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Field of view; High resolution; Image Processing and Computer Vision; Indoor environments; Probability and Statistics in Computer Science; Sensor arrays; Sensors; Special issue on Human-in-the-loop Machine Learning and its Applications; Human-centred approach; Artificial neural network; Distance estimation; Semantic segmentation; Adaptive system; Thermal sensor array
• ISSN: 0941-0643; 1433-3058; 1433-3058
• DOI: 10.1007/s00521-021-06193-2

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 ± 0.2  m in continuous-based estimation and 96.8 % achieved-accuracy in discrete distance estimation.