基于PGA的人体运动捕获数据分割方法

由于人体运动捕获数据的固有非线性，线性方法并不总是能够有效地找到运动捕获数据的内在维度，针对这种情况，提出了基于主测地线分析（PGA）和概率主测地线分析（PPGA）的自动分割方法。这两种方法都将人体运动视为一个有序的姿势序列，并在姿势序列有局部变化处对运动进行分割。基于PGA的分割方法是在运动局部模型的内在维度突然增长处分配一个分割点，基于PPGA的分割方法是在姿势分布发生改变时放置分割点。实验结果表明，该方法都能实现自动分割，且具有较好的分割结果。

Human motion capture data segmentation based on PGA

Due to the inherent nonlinearity of human motion capture data, linear methods could not always find intrinsic dimensionality of motion data, In view of this, automatic segmentation approaches using Principle Geodesic Analysis（PGA） and using Probabilistic Principle Geodesic Analysis（PPGA） are proposed in this paper. Both of these approaches regarded the motion as an ordered poses and segment the motion where there is a local change in the distribution of poses. The segmentation approach using PGA assigned a cut when the intrinsic dimensionality of a local model of the motion suddenly increased. The segmentation approach using PPGA placed a cut when the distribution of poses is observed to change. The experimental results show that they can segment automatically, and provide the good performance.