Occupancy Estimation Using Thermal Imaging Sensors and Machine Learning Algorithms

被引:39
作者
Chidurala, Veena [1 ]
Li, Xinrong [1 ]
机构
[1] Univ North Texas, Dept Elect Engn, Denton, TX 75025 USA
关键词
Sensors; Thermal sensors; Image sensors; Estimation; Imaging; Sensor systems; Sensor phenomena and characterization; Classification; infrared array sensor; occupancy estimation; thermal imaging;
D O I
10.1109/JSEN.2021.3049311
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:8627 / 8638
页数:12
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