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Color images are easy to understand visually and can acquire a great deal ofinformation, such as color and texture. They are highly and widely used intasks such as segmentation. On the other hand, in indoor person segmentation,it is necessary to collect person data considering privacy. We propose a newtask for human segmentation from invisible information, especially airborneultrasound. We first convert ultrasound waves to reflected ultrasounddirectional images (ultrasound images) to perform segmentation from invisibleinformation. Although ultrasound images can roughly identify a person'slocation, the detailed shape is ambiguous. To address this problem, we proposea collaborative learning probabilistic U-Net that uses ultrasound andsegmentation images simultaneously during training, closing the probabilisticdistributions between ultrasound and segmentation images by comparing theparameters of the latent spaces. In inference, only ultrasound images can beused to obtain segmentation results. As a result of performance verification,the proposed method could estimate human segmentations more accurately thanconventional probabilistic U-Net and other variational autoencoder models.

Invisible-to-Visible: Privacy-Aware Human Segmentation using Airborne Ultrasound via Collaborative Learning Probabilistic U-Net