Affiliations: School of Physics and Information Engineering, Fuzhou University, Fuzhou, China
Correspondence:
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Corresponding author: Jian-Wei Li, School of Physics and Information Engineering, Fuzhou University, Qi Shan Campus of Fuzhou University, 2 Xue Yuan Road, University Town, Fuzhou, Fujian 350116, China. Tel.: +86 13635269726; E-mail:lwticq@163.com
Abstract: A Motion Data Automatic Segmentation using a
Probabilistic/Kernel principal component analysis (P/KPCA) method is
proposed. This approach utilizes Kernel principal component analysis (KPCA)
to construct a kernel function while using Probabilistic principal component
analysis (PPCA) to reduce motion noise. Formulate the feature function to
obtain the derivative of projection error, and detect the segmentation point
of data through analyzing the change of geometric features to realize the
automatic segmentation. It is indicated in the experiment that the motion
capture technique has certain feasibility. The paper presents the automatic
segmentation approach of the motion capture data, in which the motion data
is automatic segmented through KPCA combined with PPCA to reduce the
dimension and project the 56 dimensional data in 2 dimensional space;
formulate the feature function to obtain the derivative of projection error,
and detect the segmentation point of data through analyzing the change of
geometric features to realize the automatic segmentation. It is indicated in
the experiment that the motion capture technique has certain feasibility.
