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.
Preview |
Text
2017 IEEE Radar Conference.pdf - Accepted Version Available under License : See the attached licence file. Download (465kB) | Preview |
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 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 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
