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Human Posture Reconstruction for Through-the-Wall Radar Imaging Using Convolutional Neural Networks
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DOI：10.1109/LGRS.2021.3073073.png)
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Abstract
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Low imaging spatial resolution hinders through-the-wall radar imaging (TWRI) from reconstructing complete human postures. This letter mainly discusses a convolutional neural network (CNN)-based human posture reconstruction method for TWRI. The training process follows a supervision-prediction learning pipeline inspired by the cross-modal learning technique. Specifically, optical images and TWRI signals are collected simultaneously using a self-develop radar containing an optical camera. Then, the optical images are processed with a computer-vision-based supervision network to generate ground-truth human skeletons. Next, the same type of skeleton is predicted from corresponding TWRI signals using a prediction network. After training, the model shows complete predictions in wall-occlusive scenarios solely using TWRI signals. Experiments show comparable quantitative results with the state-of-the-art vision-based methods in nonwall-occlusive scenarios and accurate qualitative results with wall occlusion.
Keywords:
Radar imaging
Optical imaging
Heating systems
Convolution
Image reconstruction
Feature extraction
Optical sensors
Convolutional neural network (CNN)
cross-modal learning
human posture reconstruction
through-the-wall radar imaging (TWRI)
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