Determining human pose inference using a single elevated mmWave radar

Stirling Scholes; Alice Ruget; Jonathan Leach

doi:10.1117/12.3031478

15 November 2024 Determining human pose inference using a single elevated mmWave radar

Stirling Scholes, Alice Ruget, Jonathan Leach

Proceedings Volume PC13204, Emerging Imaging and Sensing Technologies for Security and Defence IX; PC1320407 (2024) https://doi.org/10.1117/12.3031478
Event: Security + Defence, 2024, Edinburgh, United Kingdom

Abstract

Human monitoring using mmWave radar has recently become an area of significant research. The properties of radars makes them uniquely suited to imaging in adverse regimes such as through atmospheric obscurance or optically opaque media, such as housing materials. The privacy preserving nature of their data also allows radars to perform area monitoring and surveillance rolls with a reduced public impact. However, direct inference of human pose from radar data is challenging due to the relatively low transverse resolution of radar data. In this work we present a Convolutional Neural Network (CNN) capable of converting data from a commercially available Frequency Modulated Continuous Wave (FMCW) radar into human interpretable pose information. We employ a novel experimental configuration in which we combine a marker free motion capture suit with a single line sensing radar in an elevated position. We experimentally verify the ability of our system to reconstruct human pose and report average errors below 3 cm.

Conference Presentation

Citation Download Citation

Stirling Scholes, Alice Ruget, and Jonathan Leach "Determining human pose inference using a single elevated mmWave radar", Proc. SPIE PC13204, Emerging Imaging and Sensing Technologies for Security and Defence IX, PC1320407 (15 November 2024); https://doi.org/10.1117/12.3031478

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