TY - GEN
T1 - Indoor Human Activity Perception Based on Infrared Thermopile Array Sensor
AU - Zhang, Tong
AU - Yang, Bo
AU - Gu, Nanhao
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Human activity perception systems play a significant role in 'SENSEable City', smart homes and medical care, etc. Thermopile infrared array sensor (TPAS) is a new type of sensor to detect human targets without privacy invasion. This paper focuses on the perception of static and dynamic human activities. A hardware platform is built based on TPAS MLX90640 with the output of 24 times 32 pixels, to collect the thermal data of human targets from both side view and top view. After temporal Gaussian filtering, background removal, motion information extraction and data enhancement, different improved convolution neural network structures and technologies are introduced for activity perception. The best model among these networks for static and dynamic activity perception is selected and trained, achieving perception accuracy of 97.5% for static activity and 94.6% for dynamic activity respectively. The experimental results have shown that the system we proposed could correctly perceive continuous human activity and meet the real-time requirement as well.
AB - Human activity perception systems play a significant role in 'SENSEable City', smart homes and medical care, etc. Thermopile infrared array sensor (TPAS) is a new type of sensor to detect human targets without privacy invasion. This paper focuses on the perception of static and dynamic human activities. A hardware platform is built based on TPAS MLX90640 with the output of 24 times 32 pixels, to collect the thermal data of human targets from both side view and top view. After temporal Gaussian filtering, background removal, motion information extraction and data enhancement, different improved convolution neural network structures and technologies are introduced for activity perception. The best model among these networks for static and dynamic activity perception is selected and trained, achieving perception accuracy of 97.5% for static activity and 94.6% for dynamic activity respectively. The experimental results have shown that the system we proposed could correctly perceive continuous human activity and meet the real-time requirement as well.
KW - Human Activity Perception
KW - Improved Convolution Neural Network
KW - Infrared Thermopile Array Sensor
UR - https://www.scopus.com/pages/publications/85205732454
U2 - 10.1109/ICIEA61579.2024.10665115
DO - 10.1109/ICIEA61579.2024.10665115
M3 - 会议稿件
AN - SCOPUS:85205732454
T3 - 2024 IEEE 19th Conference on Industrial Electronics and Applications, ICIEA 2024
BT - 2024 IEEE 19th Conference on Industrial Electronics and Applications, ICIEA 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE Conference on Industrial Electronics and Applications, ICIEA 2024
Y2 - 5 August 2024 through 8 August 2024
ER -