Ding, C., Hong, H., Zou, Y., Chu, H., Zhu, X., Fioranelli, F. , Le Kernec, J. and Li, C. (2019) Continuous human motion recognition with a dynamic range-Doppler trajectory method based on FMCW radar. IEEE Transactions on Geoscience and Remote Sensing, 57(9), pp. 6821-6831. (doi: 10.1109/TGRS.2019.2908758)
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182990.pdf - Accepted Version 1MB |
Abstract
Radar-based human motion recognition is crucial for many applications, such as surveillance, search and rescue operations, smart homes, and assisted living. Continuous human motion recognition in real-living environment is necessary for practical deployment, i.e., classification of a sequence of activities transitioning one into another, rather than individual activities. In this paper, a novel dynamic range-Doppler trajectory (DRDT) method based on the frequency-modulated continuous-wave (FMCW) radar system is proposed to recognize continuous human motions with various conditions emulating real-living environment. This method can separate continuous motions and process them as single events. First, range-Doppler frames consisting of a series of range-Doppler maps are obtained from the backscattered signals. Next, the DRDT is extracted from these frames to monitor human motions in time, range, and Doppler domains in real time. Then, a peak search method is applied to locate and separate each human motion from the DRDT map. Finally, range, Doppler, radar cross section (RCS), and dispersion features are extracted and combined in a multidomain fusion approach as inputs to a machine learning classifier. This achieves accurate and robust recognition even in various conditions of distance, view angle, direction, and individual diversity. Extensive experiments have been conducted to show its feasibility and superiority by obtaining an average accuracy of 91.9% on continuous classification.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Fioranelli, Dr Francesco and Le Kernec, Dr Julien |
Authors: | Ding, C., Hong, H., Zou, Y., Chu, H., Zhu, X., Fioranelli, F., Le Kernec, J., and Li, C. |
College/School: | College of Science and Engineering > School of Engineering > Systems Power and Energy |
Journal Name: | IEEE Transactions on Geoscience and Remote Sensing |
Publisher: | IEEE |
ISSN: | 0196-2892 |
ISSN (Online): | 1558-0644 |
Published Online: | 23 April 2019 |
Copyright Holders: | Copyright © 2019 IEEE |
First Published: | First published in IEEE Transactions on Geoscience and Remote Sensing 57(9):6821-6831 |
Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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