TY - JOUR
T1 - Towards human distance estimation using a thermal sensor array
AU - Naser, Abdallah
AU - Lotfi, Ahmad
AU - Zhong, Junpei
N1 - Funding Information:
Abdallah Naser is supported by Nottingham Trent University through a fully funded Scholarship Scheme.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/6/15
Y1 - 2021/6/15
N2 - 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.
AB - 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.
KW - Adaptive system
KW - Artificial neural network
KW - Distance estimation
KW - Human-centred approach
KW - Semantic segmentation
KW - Thermal sensor array
UR - http://www.scopus.com/inward/record.url?scp=85107975635&partnerID=8YFLogxK
U2 - 10.1007/s00521-021-06193-2
DO - 10.1007/s00521-021-06193-2
M3 - Journal article
SN - 0941-0643
JO - Neural Computing and Applications
JF - Neural Computing and Applications
ER -