@inproceedings{f888a5a2bc83427cbdd4352e0e604279,
title = "Human Gait Recognition with Micro-Doppler Radar and Deep Autoencoder",
abstract = "The micro-Doppler signals from moving objects contain useful information about their motions. This paper introduces a novel approach for human gait recognition based on backscattered signals from a micro-Doppler radar. Three different signal techniques are utilized for the extraction of micro-Doppler features via time-frequency and time-scale representations. To classify the human motions into various types, this paper presents a deep autoencoder with the use of local patches extracted along the spectrogram and scalogram. The network configuration and the learning parameters of the deep autoencoder, which are considered as hyperparameters, are optimized by a Bayesian optimization algorithm. Experimental results produced by the proposed technique on real radar data show a significant improvement compared to several existing approaches.",
keywords = "Bayesian optimization, S-method, Short-time Fourier Transform, deep autoencoder, micro-Doppler radar, wavelet transform",
author = "Le, {Hoang Thanh} and Phung, {Son Lam} and Abdesselam Bouzerdoum",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 24th International Conference on Pattern Recognition, ICPR 2018 ; Conference date: 20-08-2018 Through 24-08-2018",
year = "2018",
month = nov,
day = "26",
doi = "10.1109/ICPR.2018.8546044",
language = "English",
series = "Proceedings - International Conference on Pattern Recognition",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3347--3352",
booktitle = "2018 24th International Conference on Pattern Recognition, ICPR 2018",
address = "United States",
}