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Human Gait Recognition with Micro-Doppler Radar and Deep Autoencoder

Conference Paper


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.

Publication Date


  • 2018

Citation


  • H. Le, S. Phung & A. Bouzerdoum, "Human Gait Recognition with Micro-Doppler Radar and Deep Autoencoder," in 24th International Conference on Pattern Recognition (ICPR 2018), 2018, pp. 3347-3352.

Scopus Eid


  • 2-s2.0-85059771241

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2243

Start Page


  • 3347

End Page


  • 3352

Place Of Publication


  • United States

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.

Publication Date


  • 2018

Citation


  • H. Le, S. Phung & A. Bouzerdoum, "Human Gait Recognition with Micro-Doppler Radar and Deep Autoencoder," in 24th International Conference on Pattern Recognition (ICPR 2018), 2018, pp. 3347-3352.

Scopus Eid


  • 2-s2.0-85059771241

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2243

Start Page


  • 3347

End Page


  • 3352

Place Of Publication


  • United States