Unsupervised Human Contour Extraction From Through-Wall Radar Images Using Dual UNet

Through-wall radar (TWR) can image the target of interest and capture the human sensing information. However, the poor human interpretability of TWR images and the lack of effective supervision make the extraction of complete body contour intractable. This letter proposes dual UNet, an unsupervised human contour extraction method for TWR images. Specifically, the method adopts two UNets with the same structure. One serves as the encoder to convert the TWR images into the latent representation. Another serves as the decoder to reconstruct the latent representation into the original images. Reconstruction loss and smooth normalized cut loss are optimized together to offset the dependence on labels and supplement global segment constraints. After training and post-processing, the latent representation can be used as the result of contour extraction. Experimental results show that dual UNet stands out among unsupervised human contour extraction methods in both free space and wall-occlusive scenarios, opening the possibility of learning useful human sensing information from raw TWR images without manual annotations.