Adaptive thermal sensor array placement for human segmentation and occupancy estimation

Naser, A ORCID: https://orcid.org/0000-0001-5969-1756, Lotfi, A ORCID: https://orcid.org/0000-0002-5139-6565 and Zhong, J ORCID: https://orcid.org/0000-0001-7642-2961, 2020. Adaptive thermal sensor array placement for human segmentation and occupancy estimation. IEEE Sensors Journal. ISSN 1530-437X

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Abstract

Thermal sensor array (TSA) offers privacy-preserving, low-cost, and non-invasive features, which makes it suitable for various indoor applications such as anomaly detection, health monitoring, home security, and monitoring energy efficiency applications. Previous approaches to human-centred applications using the TSA usually relied on the use of a fixed sensor location to make the human-sensor distance and the human presence shape fixed. However, placing this sensor in different locations and new indoor environments can pose a significant challenge. In this paper, a novel framework based on a deep convolutional encoder-decoder network is proposed to address this challenge in real-life deployment. The framework presents a semantic segmentation of the human presence and estimates the occupancy in indoor-environment. It is also capable to segment the human presence and counts the number of people from different sensor locations, indoor environments, and human to sensor distance. Furthermore, the impact of the distance on the human presence using the TSA is investigated. The framework is evaluated to estimate the occupancy in different sensor locations, the number of occupants, environments, and human distance with classification and regression machine learning approaches. This paper shows that the classification approach using the adaptive boosting algorithm is an accurate approach which has achieves an accuracy of 98.43% and 100% from vertical and overhead sensor locations respectively.

Item Type:	Journal article
Publication Title:	IEEE Sensors Journal
Creators:	Naser, A., Lotfi, A. and Zhong, J.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	31 August 2020
ISSN:	1530-437X
Identifiers:	Number Type 10.1109/jsen.2020.3020401 DOI 1362417 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	10 Sep 2020 08:36
Last Modified:	02 Sep 2022 08:29
URI:	https://irep.ntu.ac.uk/id/eprint/40656

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