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Optimised CNN for Human Counting Using Spectrograms of Probabilistic WiFi CSI | IEEE Conference Publication | IEEE Xplore

Optimised CNN for Human Counting Using Spectrograms of Probabilistic WiFi CSI


Abstract:

WiFi sensing has gained tremendous traction due to its inherent advantages in terms of privacy and ubiquity. Recent work has shown the ability to sense physical environme...Show More

Abstract:

WiFi sensing has gained tremendous traction due to its inherent advantages in terms of privacy and ubiquity. Recent work has shown the ability to sense physical environments, such as counting the number of human occupants. These results have traditionally been achieved using statistical features on WiFi Channel State Information (CSI) amplitude, however more recently there has been interest in exploiting Image based Machine Learning (ML) techniques to achieve better outcomes. In this work, we produce Probability Mass Function (PMF) Images on WiFi CSI, to create spectral maps which clearly distinguish between different human occupancies. We validate our PMF images with common default CNN architectures such as GoogleNet, ResNet and ShuffleNet. By changing the filter size and training parameters, we improve the performance of ShuffleNet from 84% to 98%. Furthermore, we demonstrate how the PMF images can be optimised for sensing outcomes, by controlling the image resolution.
Date of Conference: 04-08 December 2022
Date Added to IEEE Xplore: 11 January 2023
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Conference Location: Rio de Janeiro, Brazil

References

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