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People counting using multistatic passive WiFi radar with a multi-input deep convolutional neural network

Tang, chong; Li, wenda; Vishwakarma, Shelly; Shi, fangzhan; Julier, Simon; Chetty, Kevin; (2022) People counting using multistatic passive WiFi radar with a multi-input deep convolutional neural network. Presented at: SPIE Defense + Commercial Sensing, Orlando, FL, USA. Green open access

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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.

Type: Conference item (Presentation)
Title: People counting using multistatic passive WiFi radar with a multi-input deep convolutional neural network
Event: SPIE Defense + Commercial Sensing
Location: Orlando, FL, USA
Dates: 03 - 07 April 2022
Open access status: An open access version is available from UCL Discovery
Publisher version: https://doi.org/10.1117/12.2618234
Language: English
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10157000
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