基于谱聚类的运动捕获数据分割  

Motion Capture Data Segmentation Based on Spectral Clustering

作　　者：胡晓雁[1] 孙波[1] 朱小明[1] 魏云刚[1] 

机构地区：[1]北京师范大学信息科学与技术学院,北京100875

出　　处：《计算机辅助设计与图形学学报》2016年第8期1306-1315,共10页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61103086;61170186);中央高校基本科研业务费专项资金(105583GK);全国教育科学规划课题(DCA140229)

摘　　要：为将长运动中所包含的不同运动自动分割出来,提出一种基于谱聚类的长运动数据分割算法.首先将运动捕获数据分解成长度相等的运动数据片段,并基于主成分分析来计算这些小片段之间的相似度,得到运动数据相似度矩阵;然后用谱聚类算法将相似度矩阵转换为相应的拉普拉斯矩阵,计算其前若干个特征向量,并采用K均值算法获得聚类结果;由于上述相似度矩阵直接实施谱聚类算法所得到的类别标签序列包含大量噪声,采用统计滤波算法对分类结果进行了处理,获得最终的分割点.在14个运动数据上进行自动分割测试,验证了文中算法的有效性.A long motion capture data often contains several different motions or the same motion repeats several times. It is an important topic to segment a long motion capture data into different motions. In this paper, we propose a method to segment long motions into several different motions by using a spectral clustering algorithm. When computing similarity matrices, we first cut the original motion capture data into mocap clips and each clip contains k frames mocap data. We then apply PCA dimension reduction technique on each mocap clips and compute similarities between these clips. By doing so, our similarity measurement takes the motion continuity into account. Moreover, we also greatly improve the efficiency by avoiding frame by frame similarity computation which is much more time consuming. When applying spectral clustering on the similarity matrix directly, the resulting classification labels are with serious noises. To address this problem, we propose to use median filter to remove the noises, and get good segmentation points. The automatic segmentation results on 14 motion data demonstrate the effectiveness of the proposed method.

关 键 词：运动捕获数据 谱聚类 运动分割 主成分分析 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]