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A Robust mmWave Radar Framework for Accurate People Counting and Motion Classification.

Nuobei Zhang1, Haoxuan Li1, Adnan Zahid1

  • 1School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh EH14 4AS, UK.

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PubMed
Summary
This summary is machine-generated.

This study introduces a privacy-preserving millimeter-wave (mmWave) radar system for accurate people counting and occupancy monitoring. The system uses deep learning to achieve high accuracy, offering a reliable solution for smart cities.

Keywords:
indoor localizationmachine learningmmWave radarpeople counting

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Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Conventional people counting methods (camera, infrared) face privacy, lighting, and robustness issues.
  • Intelligent building management and smart city applications require reliable occupancy monitoring.
  • Millimeter-wave (mmWave) radar offers a privacy-preserving alternative for indoor sensing.

Purpose of the Study:

  • To develop and evaluate a novel 60 GHz mmWave radar system for accurate and privacy-preserving people counting.
  • To address the limitations of existing occupancy monitoring technologies.
  • To provide a robust solution for intelligent indoor environments.

Main Methods:

  • Utilized 60 GHz mmWave radar to capture echo signals.
  • Processed signals into Doppler and range spectrograms.
  • Employed an enhanced ResNet-50 deep learning model for motion state classification and people counting.

Main Results:

  • Achieved 95.45% accuracy in classifying 6 different motion states.
  • Demonstrated 98.86% accuracy for people counting (0-3 persons).
  • Showcased adaptability with limited data and robustness against Gaussian blur.

Conclusions:

  • The mmWave radar system provides an efficient and reliable method for privacy-preserving occupancy monitoring.
  • The enhanced ResNet-50 model effectively analyzes radar data for accurate people counting.
  • This technology offers a promising solution for smart city applications requiring indoor monitoring.