Towards human distance estimation using a thermal sensor array

Abdallah Naser (Corresponding Author), Ahmad Lotfi, Junpei Zhong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

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.

Original languageEnglish
JournalNeural Computing and Applications
DOIs
Publication statusPublished - 15 Jun 2021

Keywords

  • Adaptive system
  • Artificial neural network
  • Distance estimation
  • Human-centred approach
  • Semantic segmentation
  • Thermal sensor array

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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