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Joint Fall And Aspect Angle Recognition Using Fine-Grained Micro-Doppler Classification

Chen, Q; Ritchie, M; Liu, Y; Chetty, K; Woodbridge, K; (2017) Joint Fall And Aspect Angle Recognition Using Fine-Grained Micro-Doppler Classification. In: Proceedings of the 2017 IEEE Radar Conference (RadarConf). (pp. pp. 912-916). IEEE: Seattle, WA, USA. Green open access

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Abstract

Activity recognition and monitoring are finding important applications in ambient assisted living healthcare. Among the various types of motions which researchers are attempting to detect and recognize, fall detection has gained significant interest. In this paper, we investigate the application of high-frequency (24 GHz) FMCW radar for multi-perspective micro-Doppler (μ-D) activity recognition. Data from two different types of motion; falling and picking-up an object, were collected from three aspect angles and put through a fine-grained classifier to not only differentiate the motions, but to also identify their aspect towards the radar receivers. A key novel component of this work is the application of the fine-grained classification task, where a label discriminate sparse representation classifier is proposed to improve recognition performance over very similar μ-D signatures. This is achieved by learning a discriminate dictionary constrained by the label information and meanwhile preventing the overfitting problem. The greatest increase in classification performance was found to be of the order of 8 %.

Type: Proceedings paper
Title: Joint Fall And Aspect Angle Recognition Using Fine-Grained Micro-Doppler Classification
Event: 2017 IEEE Radar Conference (RadarConf)
Location: Seattle, WA
Dates: 08 May 2017 - 12 May 2017
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/RADAR.2017.7944333
Publisher version: https://doi.org/10.1109/RADAR.2017.7944333
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: Training, Dictionaries, Radar, Time-frequency analysis, Encoding, Sparse matrices, Geometry, Fall Recognition, Fine-Grained Micro-Doppler Classification, Aspect Angles, Sparse Representation Classifier, Label Discriminate
UCL classification: 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
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/1551639
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