University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam.
National Centre for Computer Animation, Bournemouth University, Poole, United Kingdom.
PLoS One. 2022 Aug 8;17(8):e0272407. doi: 10.1371/journal.pone.0272407. eCollection 2022.
"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.
“缺失标记问题”，即在运动捕捉过程中丢失标记，在运动捕捉领域已经提出了多年。我们提出了局部加权主成分分析（PCA）回归方法来处理这一挑战。其主要优点是通过多元缩径方法将观测数据集的稀疏性引入传统的最小二乘方法，并将其发展成为一种具有稀疏约束的新的最小二乘方法。据我们所知，这是第一个具有稀疏约束的最小二乘方法。我们的实验表明，所提出的回归方法可以达到较高的估计精度，并且具有良好的数值稳定性。
