mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor

Kevin Mitchell, Khaled Kassem, Chaitanya Kaul, Valentin Kapitany, Philip Binner +3 more
2/28/2023
cs.LGeess.SP

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Abstract

For widespread adoption, public security and surveillance systems must be accurate, portable, compact, and real-time, without impeding the privacy of the individuals being observed. Current systems broadly fall into two categories -- image-based which are accurate, but lack privacy, and RF signal-based, which preserve privacy but lack portability, compactness and accuracy. Our paper proposes mmSense, an end-to-end portable miniaturised real-time system that can accurately detect the presence of concealed metallic objects on persons in a discrete, privacy-preserving modality. mmSense features millimeter wave radar technology, provided by Google's Soli sensor for its data acquisition, and TransDope, our real-time neural network, capable of processing a single radar data frame in 19 ms. mmSense achieves high recognition rates on a diverse set of challenging scenes while running on standard laptop hardware, demonstrating a significant advancement towards creating portable, cost-effective real-time radar based surveillance systems.

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Cite this paper

@article{mitchell2023mmsense,
  title  = {mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor},
  author = {Kevin Mitchell and Khaled Kassem and Chaitanya Kaul and Valentin Kapitany and Philip Binner and Andrew Ramsay and Roderick Murray-Smith and Daniele Faccio},
  year   = {2023},
  eprint = {2302.14625},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.14625v1}
}

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