mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor

• URL: http://arxiv.org/abs/2302.14625v1
• Date: Tue, 28 Feb 2023 15:06:03 GMT
• Title: mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor
• Authors: Kevin Mitchell, Khaled Kassem, Chaitanya Kaul, Valentin Kapitany, Philip Binner, Andrew Ramsay, Roderick Murray-Smith, Daniele Faccio
• Abstract summary: mmSense is an end-to-end portable miniaturised real-time system that can accurately detect the presence of concealed metallic objects on persons. 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.
• Score: 2.963928676363629
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar [62.51065633674272]
  We introduce Radar Fields - a neural scene reconstruction method designed for active radar imagers. Our approach unites an explicit, physics-informed sensor model with an implicit neural geometry and reflectance model to directly synthesize raw radar measurements. We validate the effectiveness of the method across diverse outdoor scenarios, including urban scenes with dense vehicles and infrastructure.
  arXiv  Detail & Related papers  (2024-05-07T20:44:48Z)
• Vision Guided MIMO Radar Beamforming for Enhanced Vital Signs Detection in Crowds [26.129503530877006]
  We develop a novel dual-sensing system, in which a vision sensor is leveraged to guide digital beamforming in a radar. The calibrated dual system achieves about two centimeters precision in three-dimensional space within a field of view of $75circ$ by $65circ$ and for a range of two meters.
  arXiv  Detail & Related papers  (2023-06-18T10:09:16Z)
• A Systematic Study on Object Recognition Using Millimeter-wave Radar [1.3192560874022086]
  millimeter-wave (MMW) radars are essential in smart environments. MMW radars are expensive and hard to get for community-purpose smart environment applications. These challenges need to be investigated for tasks like recognizing objects and activities.
  arXiv  Detail & Related papers  (2023-05-03T12:42:44Z)
• Interpersonal Distance Tracking with mmWave Radar and IMUs [5.520108182364194]
  ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and distances tracing. Evaluation shows ImmTrack deci-identifications decitemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance.
  arXiv  Detail & Related papers  (2023-02-28T15:44:17Z)
• DensePose From WiFi [86.61881052177228]
  We develop a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. Our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches.
  arXiv  Detail & Related papers  (2022-12-31T16:48:43Z)
• Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities [66.4525391417921]
  We design and evaluate a multi-sensor drone detection system. Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest. The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution.
  arXiv  Detail & Related papers  (2022-07-05T10:00:58Z)
• Learning to Detect Open Carry and Concealed Object with 77GHz Radar [7.608789301874509]
  This paper focuses on the relatively unexplored area of using low-cost 77GHz mmWave radar for the carried objects detection problem. The proposed system is capable of real-time detecting three classes of objects - laptop, phone, and knife - under open carry and concealed cases. This system would be the very first baseline for other future works aiming to detect carried objects using 77GHz radar.
  arXiv  Detail & Related papers  (2021-10-31T17:33:28Z)
• Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation [73.0103413636673]
  We propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. CVCNNs increase data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.
  arXiv  Detail & Related papers  (2021-04-29T10:06:29Z)
• Demo Abstract: Indoor Positioning System in Visually-Degraded Environments with Millimetre-Wave Radar and Inertial Sensors [44.58134907168034]
  We present a real-time indoor positioning system which fuses millimetre-wave (mmWave) radar and Inertial Measurement Units (IMU) data via deep sensor fusion. Good accuracy and resilience were exhibited even in poorly illuminated scenes.
  arXiv  Detail & Related papers  (2020-10-26T17:41:25Z)
• RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects [73.80316195652493]
  We tackle the problem of exploiting Radar for perception in the context of self-driving cars. We propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion.
  arXiv  Detail & Related papers  (2020-07-28T17:15:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.