Yang, L;
Li, G;
Ritchie, M;
Fioranelli, F;
Griffiths, H;
(2017)
Gait classification based on micro-Doppler features.
In:
2016 CIE International Conference on Radar (RADAR).
IEEE
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Abstract
This paper focuses on the classification of human gaits based on micro-Doppler signatures. The micro-Doppler signatures can represent detailed information about the human gaits, which helps in judging the threat of a personnel target. The proposed method consists of three major steps. Firstly, the micro-Doppler signatures are obtained by performing time-frequency analysis on the radar data. Then two micro-Doppler features are extracted from the time-frequency domain. Finally, the one-versus-one support vector machine (SVM) is used to realize multi-class classification. Experiments on real data show that, with the selected features, high classification accuracy of the human gaits of interest can be achieved.
| Type: | Proceedings paper |
|---|---|
| Title: | Gait classification based on micro-Doppler features |
| Event: | 2016 CIE International Conference on Radar (RADAR) |
| ISBN-13: | 9781509048281 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/RADAR.2016.8059301 |
| Publisher version: | https://doi.org/10.1109/RADAR.2016.8059301 |
| 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: | Micro-Doppler, classification, human gait, time-frequency analysis, support vector machine |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices 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/10051765 |
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