Ton-Tran, K;
Griffin, L;
Chetty, K;
(2020)
Transfer Learning from Audio Deep Learning Models for Micro-Doppler Activity Recognition.
In:
Proceedings of the 2020 IEEE Radar Conference.
IEEE: Florence, Italy.
(In press).
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Abstract
This paper presents a mechanism to transform radio micro-Doppler signatures into a pseudo-audio representation, which results in significant improvements in transfer learning from a deep learning model trained on audio. We also demonstrate that transfer learning from a deep learning model trained on audio is more effective than transfer learning from a model trained on images, which suggests machine learning methods used to analyse audio can be leveraged for micro-Doppler. Finally, we utilise an occlusion method to gain an insight into how the deep learning model interprets the micro-Doppler signatures and the subsequent pseudo-audio representations.
| Type: | Proceedings paper |
|---|---|
| Title: | Transfer Learning from Audio Deep Learning Models for Micro-Doppler Activity Recognition |
| Event: | 2020 IEEE Radar Conference |
| Location: | Washington, USA |
| Dates: | 27 April 2020 - 30 April 2020 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.radarconf20.org/ |
| 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 signatures, activity recognition, transfer learning |
| 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 Computer Science 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/10093365 |
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