TY - GEN
T1 - Human Motion Classification with Micro-Doppler Radar and Bayesian-Optimized Convolutional Neural Networks
AU - Le, Hoang Thanh
AU - Phung, Son Lam
AU - Bouzerdoum, Abdesselam
AU - Tivive, Fok Hing Chi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - In recent years, Doppler radar has emerged as an alternative sensing modality for human gait classification since it measures not only the target speed, but also the local dynamics of the moving body parts, thereby creating a unique spectral signature. This paper presents a learning-based method for classifying human motions from micro-Doppler signals. Inspired by the applications of deep learning, the proposed method extracts features from the time-frequency representation of the radar signal using a cascaded of convolutional network layers. To design a optimal network architecture, the Bayesian optimization with Gaussian process priors is employed. Experimental results on real data are presented, which show a significant improvement compared to three existing approaches.
AB - In recent years, Doppler radar has emerged as an alternative sensing modality for human gait classification since it measures not only the target speed, but also the local dynamics of the moving body parts, thereby creating a unique spectral signature. This paper presents a learning-based method for classifying human motions from micro-Doppler signals. Inspired by the applications of deep learning, the proposed method extracts features from the time-frequency representation of the radar signal using a cascaded of convolutional network layers. To design a optimal network architecture, the Bayesian optimization with Gaussian process priors is employed. Experimental results on real data are presented, which show a significant improvement compared to three existing approaches.
KW - Bayesian optimization
KW - Convolutional neural network (CNN)
KW - Micro-Doppler radar
KW - Time-frequency representation
UR - https://www.scopus.com/pages/publications/85054215757
U2 - 10.1109/ICASSP.2018.8461847
DO - 10.1109/ICASSP.2018.8461847
M3 - Conference contribution
AN - SCOPUS:85054215757
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2961
EP - 2965
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -