Hierarchical radar data analysis for activity and personnel recognition

Li, X., Li, Z., Fioranelli, F. , Yang, S. , Romain, O. and Le Kernec, J. (2020) Hierarchical radar data analysis for activity and personnel recognition. Remote Sensing, 12(14), 2237. (doi: 10.3390/rs12142237)

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Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find the algorithm (features plus classifier) that can deal with multiple activities analysed in one study such as walking, sitting, drinking and crawling. However, using the same set of features for multiple activities can be suboptimal per activity and not take into account the diversity of kinematic movements that could be captured by diverse features. In this paper, we propose a hierarchical classification approach that uses a large variety of features including but not limited to energy features like entropy and energy curve, physical features like centroid and bandwidth, image-based features like skewness extracted from multiple radar data domains. Feature selection is used at each step of the hierarchical model to select the best set of features to discriminate the target activity from the others, showing improvements with respect to the more conventional approach of using a multiclass model. The proposed approach is validated on a large dataset with 1078 recorded samples of varying length from 5 s to 10 s of experimental data, yielding 95.4% accuracy to classify six activities. The approach is also validated on a personnel recognition task to identify individual subjects from their walking gait, yielding 83.7% accuracy for ten subjects and 68.2% for a significantly larger group of subjects, i.e., 60 people.

Item Type:Articles
Additional Information:This research was funded by The authors would like to thank the British Council 515095884 and Campus 386 France 44764WK – PHC Alliance France-UK for their financial support.
Glasgow Author(s) Enlighten ID:Romain, Professor Olivier and Yang, Dr Shufan and Fioranelli, Dr Francesco and Le Kernec, Dr Julien and Li, Zhenghui
Authors: Li, X., Li, Z., Fioranelli, F., Yang, S., Romain, O., and Le Kernec, J.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Remote Sensing
ISSN (Online):2072-4292
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in Remote Sensing 12(14):2237
Publisher Policy:Reproduced under a Creative Commons License

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