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Transfer Learning from Audio Deep Learning Models for Micro-Doppler Activity Recognition

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). Green open access

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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 > 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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