TY - GEN
T1 - Human Activity Classification Method Using a Generalized Recurrent Neural Network
AU - Mao, Tong
AU - Zhao, Guoqiang
AU - Sun, Houjun
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Millimeter wave radar offers advantages in scene surveillance, traffic monitoring and health monitoring due to its penetrability and privacy. Abnormal human behaviors could be identified through the radar detection and classification process. In this paper, an abnormal human activity classification method based on micro-Doppler effect is proposed. The singular vector decomposition (SVD) and principle component analysis (PCA) are extracted from simulated radar echo and fed into a Generalized Regression Neural Network (GRNN) for classification.
AB - Millimeter wave radar offers advantages in scene surveillance, traffic monitoring and health monitoring due to its penetrability and privacy. Abnormal human behaviors could be identified through the radar detection and classification process. In this paper, an abnormal human activity classification method based on micro-Doppler effect is proposed. The singular vector decomposition (SVD) and principle component analysis (PCA) are extracted from simulated radar echo and fed into a Generalized Regression Neural Network (GRNN) for classification.
UR - http://www.scopus.com/inward/record.url?scp=85080100172&partnerID=8YFLogxK
U2 - 10.1109/ICMMT45702.2019.8992580
DO - 10.1109/ICMMT45702.2019.8992580
M3 - Conference contribution
AN - SCOPUS:85080100172
T3 - 2019 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2019 - Proceedings
BT - 2019 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2019
Y2 - 19 May 2019 through 22 May 2019
ER -
