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13 June 2022 People counting using multistatic passive WiFi radar with a multi-input deep convolutional neural network
Chong Tang, Wenda Li, Shelly Vishwakarma, Fangzhan Shi, Simon Julier, Kevin Chetty
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Proceedings Volume PC12108, Radar Sensor Technology XXVI; (2022) https://doi.org/10.1117/12.2618234
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States
Abstract
Accurately counting numbers people is useful in many applications. Currently, camera-based systems assisted by computer vision and machine learning algorithms represent the state-of-the-art. However, they have limited coverage areas and are prone to blind spots, obscuration by walls, shadowing of individuals in crowds, and rely on optimal positioning and lighting conditions. Moreover, their ability to image people raises ethical and privacy concerns. In this paper we propose a distributed multistatic passive WiFi radar (PWR) consisting of 1 reference and 3 surveillance receivers, that can accurately count up to six test subjects using Doppler frequency shifts and intensity data from measured micro-Doppler (µ-Doppler) spectrograms. To build the person-counting processing model, we employ a multi-input convolutional neural network (MI-CNN). The results demonstrate a 96% counting accuracy for six subjects when data from all three surveillance channels are utilised.
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Chong Tang, Wenda Li, Shelly Vishwakarma, Fangzhan Shi, Simon Julier, and Kevin Chetty "People counting using multistatic passive WiFi radar with a multi-input deep convolutional neural network", Proc. SPIE PC12108, Radar Sensor Technology XXVI, (13 June 2022); https://doi.org/10.1117/12.2618234
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KEYWORDS
Radar
Convolutional neural networks
Feature extraction
Fourier transforms
Neural networks
Receivers
Surveillance
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