Locally weighted PCA regression to recover missing markers in human motion data.

Autor: Kieu HD; University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam., Yu H; National Centre for Computer Animation, Bournemouth University, Poole, United Kingdom., Li Z; School of Computer Science, Zhejiang University City College, Hangzhou, China., Zhang JJ; National Centre for Computer Animation, Bournemouth University, Poole, United Kingdom.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2022 Aug 08; Vol. 17 (8), pp. e0272407. Date of Electronic Publication: 2022 Aug 08 (Print Publication: 2022).
DOI: 10.1371/journal.pone.0272407
Abstrakt: "Missing markers problem", that is, missing markers during a motion capture session, has been raised for many years in Motion Capture field. We propose the locally weighted principal component analysis (PCA) regression method to deal with this challenge. The main merit is to introduce the sparsity of observation datasets through the multivariate tapering approach into traditional least square methods and develop it into a new kind of least square methods with the sparsity constraints. To the best of our knowledge, it is the first least square method with the sparsity constraints. Our experiments show that the proposed regression method can reach high estimation accuracy and has a good numerical stability.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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