TY - GEN
T1 - Rgb-Thermal imaging system collaborated with marker tracking for remote breathing rate measurement
AU - Chen, Lushuang
AU - Liu, Ning
AU - Hu, Menghan
AU - Zhai, Guangtao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - The pixel variation signal extracted from the nasal region of RGB-Thermal images can be used to achieve breathing rate (BR) measurement. However, this method fails when the nasal region is not detected in complicated motion scenarios. In this paper, we develop an RGB-Thermal imaging system collaborated with marker sticker to achieve unobtrusive and accurate BR measurement. Pixel variation signal of Regions of interest (ROI) is extracted from the thermal video and chest movement signal is extracted from the RGB video with the assistance of marker stickers. Subsequently, a custom-made time-domain signal processing approach is developed for determining BR. We further propose a method of splicing computation to measure the BR after separate processing of signal segments. We construct an RGB-Thermal video dataset with different head and body movements to evaluate the effectiveness of the proposed algorithm. After linear regression analysis, the determination coefficient (R2) of 0.905 has been observed for the estimated and reference BRs, indicating the feasibility of our proposed method in complex motion scenarios.
AB - The pixel variation signal extracted from the nasal region of RGB-Thermal images can be used to achieve breathing rate (BR) measurement. However, this method fails when the nasal region is not detected in complicated motion scenarios. In this paper, we develop an RGB-Thermal imaging system collaborated with marker sticker to achieve unobtrusive and accurate BR measurement. Pixel variation signal of Regions of interest (ROI) is extracted from the thermal video and chest movement signal is extracted from the RGB video with the assistance of marker stickers. Subsequently, a custom-made time-domain signal processing approach is developed for determining BR. We further propose a method of splicing computation to measure the BR after separate processing of signal segments. We construct an RGB-Thermal video dataset with different head and body movements to evaluate the effectiveness of the proposed algorithm. After linear regression analysis, the determination coefficient (R2) of 0.905 has been observed for the estimated and reference BRs, indicating the feasibility of our proposed method in complex motion scenarios.
KW - Motion-based breathing detection
KW - RGB-Thermal imaging
KW - Respiration measurement
KW - Splicing calculation process
UR - https://www.scopus.com/pages/publications/85079227431
U2 - 10.1109/VCIP47243.2019.8965987
DO - 10.1109/VCIP47243.2019.8965987
M3 - 会议稿件
AN - SCOPUS:85079227431
T3 - 2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019
BT - 2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th IEEE International Conference on Visual Communications and Image Processing, VCIP 2019
Y2 - 1 December 2019 through 4 December 2019
ER -