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Gait classification based on micro-Doppler features

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 Green open access

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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
URI: http://discovery.ucl.ac.uk/id/eprint/10051765
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