In-bed pose estimation is of great value in current health-monitoring systems. In this paper, we solve a crossdomain pose estimation problem, in which a fully annotated uncovered training set is used for pose estimation learning, and a large-scale unlabelled data set of covered images is employed for unsupervised domain adaptation. To tackle this challenging problem, we propose a multi-level domain adaptation framework, which learns a generalizable pose estimation network based three levels of adaptation. We evaluate the proposed framework on a public in-bed pose estimation benchmark. The results demonstrate that our proposed framework can effectively generalize the learned knowledge from the uncovered source domain to the covered target domain for privacy-protected in-bed pose estimation.
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