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

doi:10.1117/12.2618234

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

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.

Conference Presentation

Citation Download Citation

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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