mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor
Kevin J. Mitchell, Khaled Kassem, Chaitanya Kaul, Valentin Kapitany, Philip Binner, Andrew Ramsay, Daniele Faccio, Roderick Murray-Smith
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. mm-Sense 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.
BibTeX
@inproceedings{icassp2023_mmsensedetecting,
title = {mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor},
author = {Kevin J. Mitchell and Khaled Kassem and Chaitanya Kaul and Valentin Kapitany and Philip Binner and Andrew Ramsay and Daniele Faccio and Roderick Murray-Smith},
booktitle = {ICASSP 2023},
year = {2023}
}