Li, G;
Zhang, R;
Ritchie, M;
Griffiths, H;
(2017)
Sparsity-based dynamic hand gesture recognition using micro-Doppler signatures.
In:
2017 IEEE Radar Conference (RadarConf).
(pp. 0928-0931).
IEEE
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Abstract
In this paper, a sparsity-driven method of micro-Doppler analysis is proposed for dynamic hand gesture recognition with radar sensor. The sparse representation of the radar signal in the time-frequency domain is achieved through the Gabor dictionary, and then the micro-Doppler features are extracted by using the orthogonal matching pursuit (OMP) algorithm and fed into classifiers for dynamic hand gesture recognition. The proposed method is validated with real data measured with a K-band radar. Experiment results show that the proposed method outperforms the principal component analysis (PCA) algorithm, with the recognition accuracy higher than 90%.
| Type: | Proceedings paper |
|---|---|
| Title: | Sparsity-based dynamic hand gesture recognition using micro-Doppler signatures |
| Event: | 2017 IEEE Radar Conference (RadarConf) |
| ISBN-13: | 9781467388238 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/RADAR.2017.7944336 |
| Publisher version: | https://doi.org/10.1109/RADAR.2017.7944336 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Dynamic hand gesture recognition, micro-Doppler analysis, sparse signal representation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10051768 |
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