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People Counting Solution Using an FMCW Radar with Knowledge Distillation From Camera Data

Stephan M, Hazra S, Santra A, Weigel R, Fischer G (2021)

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Publication Type: Conference contribution

Publication year: 2021

Publisher: IEEE

City/Town: NEW YORK

Conference Proceedings Title: 2021 IEEE SENSORS

DOI: 10.1109/SENSORS47087.2021.9639798

Abstract

Radar systems enable remote sensing of multiple persons within their field of view. In this paper, we propose a novel architecture to perform people counting using a 60 GHz Frequency Modulated Continuous Wave radar trained on supervised radar data and knowledge distillation performed using synchronized camera data. In the evaluation phase, only the radar encoder with Range - Doppler Images (RDI) as input is used and tested on a dataset consisting of scenarios recorded in a different setup than the training recordings with up to 6 persons present. In this paper we focus on showing the benefit of using the cross-modal camera information compared to the same unimodal model. In spite of the low-cost radar sensor, the proposed architecture achieves an accuracy of 71% compared to 58% for the test data from a different sensor with a different orientation and aspect angle, and an accuracy of 89% compared to 74% for test data from the same radar sensor when training without knowledge distillation.

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How to cite

APA:

Stephan, M., Hazra, S., Santra, A., Weigel, R., & Fischer, G. (2021). People Counting Solution Using an FMCW Radar with Knowledge Distillation From Camera Data. In 2021 IEEE SENSORS. NEW YORK: IEEE.

MLA:

Stephan, Michael, et al. "People Counting Solution Using an FMCW Radar with Knowledge Distillation From Camera Data." Proceedings of the 20th IEEE Sensors Conference NEW YORK: IEEE, 2021.

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