CAJ | 학술논문

针对运动捕获数据的高效匹配问题，提出了一种新的基于四元数描述和EMD( Earth Mover's Distance)的人体运动检索算法。该算法主要包括特征提取和运动匹配两部分。在特征提取部分，为了解决高维数据检索效率低的问题，引入了四元数描述符对关节点的数据信息特征进行描述，通过映射姿态分布的原始数据，并采取K-means聚类方法对待查询动作和运动数据库的特征数据进行降维并归类。在运动匹配部分，根据聚类结果，建立每个特征数据集的距离矩阵，将匹配问题转换为运输优化问题。然后，用EMD算法度量待查询动作和数据库动作之间的相似值。仿真实验结果证明了提出的算法是有效的。
침대운동포획수거적고효필배문제，제출료일충신적기우사원수묘술화EMD( Earth Mover's Distance)적인체운동검색산법。해산법주요포괄특정제취화운동필배량부분。재특정제취부분，위료해결고유수거검색효솔저적문제，인입료사원수묘술부대관절점적수거신식특정진행묘술，통과영사자태분포적원시수거，병채취K-means취류방법대대사순동작화운동수거고적특정수거진행강유병귀류。재운동필배부분，근거취류결과，건립매개특정수거집적거리구진，장필배문제전환위운수우화문제。연후，용EMD산법도량대사순동작화수거고동작지간적상사치。방진실험결과증명료제출적산법시유효적。
A novel retrieval approach has been put forward based on quaternion and EMD ( Earth Mover's Distance) for efficient matc-hing motion capture data. The method mainly contains two steps:feature extracting and motion matching. In feature extracting part,for solving the problem of low efficiency retrieval of high-dimensional data,introduce the quaternion to represent key-joints rotation data in-formation,through the mapping original data of distribution,take K-means clustering methods to reduce the dimension and classify for the feature data in database of query motion and movement. In motion matching part,according to the clustering results,the distance matrix of each feature dataset was established,converting the matching problem of motion capture data into a optimization transportation problem. Then,the EMD measure algorithm was employed to query the similar value between query motions and database motions. Experiment re-sults show that the proposed approach is efficient.