Multi-level unsupervised domain adaption for privacy-protected in-bed pose estimation

Ziheng Chi; Shaozhi Wang; Xinyue Li; Chun-Tzu Chang; Md Islam; Akshay Holkar; Samantha Pronger; Tianshan Liu; Kin-Man Lam; Xiangjian He

doi:10.1117/12.2626114

30 April 2022 Multi-level unsupervised domain adaption for privacy-protected in-bed pose estimation

Ziheng Chi, Shaozhi Wang, Xinyue Li, Chun-Tzu Chang, Md Islam, Akshay Holkar, Samantha Pronger, Tianshan Liu, Kin-Man Lam, Xiangjian He

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Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 1217726 (2022) https://doi.org/10.1117/12.2626114
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

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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Ziheng Chi, Shaozhi Wang, Xinyue Li, Chun-Tzu Chang, Md Islam, Akshay Holkar, Samantha Pronger, Tianshan Liu, Kin-Man Lam, and Xiangjian He "Multi-level unsupervised domain adaption for privacy-protected in-bed pose estimation", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 1217726 (30 April 2022); https://doi.org/10.1117/12.2626114

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