TY - GEN
T1 - A 3D CNN based People Counting System Using Auto-Correlation Functions from Frequency Modulated Continuous Wave Radar Signals
AU - Seo, Yura
AU - Han, Miseon
AU - Kim, Jeongtae
N1 - Funding Information:
This work is supported by grants from TOP central R&D company and Powerlogics company in Korea.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We propose a people counting system using Frequency Modulated Continuous Wave (FMCW) radar signals. The proposed method predicts the number of people using deep learning classification with auto-correlation functions of estimated breathing signals. We also attempted to explain the behavior of the deep learning system using modified gradient-weighted class activation mapping (GradCAM). In the experiments using real radar signals, the proposed method showed improved performance from a conventional method.
AB - We propose a people counting system using Frequency Modulated Continuous Wave (FMCW) radar signals. The proposed method predicts the number of people using deep learning classification with auto-correlation functions of estimated breathing signals. We also attempted to explain the behavior of the deep learning system using modified gradient-weighted class activation mapping (GradCAM). In the experiments using real radar signals, the proposed method showed improved performance from a conventional method.
UR - http://www.scopus.com/inward/record.url?scp=85144034557&partnerID=8YFLogxK
U2 - 10.1109/SENSORS52175.2022.9967284
DO - 10.1109/SENSORS52175.2022.9967284
M3 - Conference contribution
AN - SCOPUS:85144034557
T3 - Proceedings of IEEE Sensors
BT - 2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Sensors Conference, SENSORS 2022
Y2 - 30 October 2022 through 2 November 2022
ER -