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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationRadar Sensor Technology XXVI
Subtitle of host publicationAt SPIE Defense + Commercial Sensing
EditorsKenneth I. Ranney, Ann M. Raynal
Place of PublicationOrlando
PublisherSPIE-International Society for Optical Engineering
ISBN (Print)9781510650923
Publication statusPublished - 13 Jun 2022
EventSPIE Defense + Commercial Sensing 2022 - Orlando, United States
Duration: 3 Apr 20227 Apr 2022


ConferenceSPIE Defense + Commercial Sensing 2022
Country/TerritoryUnited States
Internet address


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