Chenning Li
Zhichao Cao
Mi Zhang
Yunhao Liu
ABSTRACT
Wi-Fi imaging has attracted significant interests due to the ubiquitous availability of Wi-Fi devices today. In this paper, we present Wi-Fi See It All (WiSIA), a versatile Wi-Fi imaging system built upon commercial off-the-shelf (COTS) Wi-Fi devices, which is able to simultaneously detect objects and humans, segment their boundaries, and identify them within the image plane. To achieve this, WiSIA utilizes three techniques. First, instead of constructing the image plane at the receiver side using a high-cost antenna array and complex parameter estimation, WiSIA pushes the image plane to the object side with two pairs of transceivers and 2D-IFFT. Second, WiSIA extracts the specific physical signature of the signals reflected from multiple objects to segment their boundaries. Third, WiSIA incorporates a cGAN (conditional Generative Adversarial Network) to enhance the boundary of different objects. We have implemented WiSIA using COTS Wi-Fi devices and evaluated it using a rich set of experiments. Our results demonstrate the efficacy of WiSIA. It outperforms the state-of-the-art vision-based method in dark and occlusion scenarios, demonstrating its superiority in such challenge scenarios.
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Index Terms
Wi-fi see it all: generative adversarial network-augmented versatile wi-fi imaging
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