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Occupancy Estimation Using Thermal Imaging Sensors and Machine Learning Algorithms.

Authors :
Chidurala, Veena
Li, Xinrong
Source :
IEEE Sensors Journal; 3/15/2021, Vol. 21 Issue 6, p8627-8638, 12p
Publication Year :
2021

Abstract

Occupancy estimation has a broad range of applications in security, surveillance, traffic and resource management in smart building environments. Low-resolution thermal imaging sensors can be used for real-time non-intrusive occupancy estimation. Such sensors have a resolution that is too low to identify occupants, but it may provide sufficient data for real-time occupancy estimation. In this paper, we present a systematic study of three thermal imaging sensors with different resolutions, with a focus on sensor characterization, estimation algorithms, and comparative analysis of occupancy estimation performance. A unified processing algorithms pipeline for occupancy estimation is presented and the performance of three sensors are compared side-by-side. A number of specific algorithms are proposed for pre-processing of sensor data, feature extraction, and fine-tuning of the occupancy estimation algorithms. Our results show that it is possible to achieve about 99% accuracy for occupancy estimation with our proposed approach, which might be sufficient for many practical smart building applications. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1530437X
Volume :
21
Issue :
6
Database :
Complementary Index
Journal :
IEEE Sensors Journal
Publication Type :
Academic Journal
Accession number :
148969804
Full Text :
https://doi.org/10.1109/JSEN.2021.3049311