Automatic Transition Detection of Segmented Motion Clips Using PCA-based GMM Method
Yan Wang, Hyewon Seo, Soohyun Jeon · 2008
In this work, we record a dancer's rhythmic movement with background music. The captured motion sequences are then segmented into dozens of motion clips, to construct a motion database consisting of sets of labeled motion clips. Many of these motion clips contain short and rapid transition from one main dancing motion to another, which causes unnatural, awkward movements when they are connected in different orders than the original sequence. In this paper, we describe our approach for automatically detecting the transition parts in the segmented motion clips. For each motion clip, we model the motion data using the Gaussian mixture model (GMM) and use the resulting distribution cluster map to improve the efficiency and convergence of the clustering, principal component analysis (PCA) has been applied to the motion data prior to performing GMM. Experiments and comparative analysis show that this PCA-based GMM method effectively performs transition detection on the segmented motion clips.