基于优化的DTW算法的人体运动数据检索

Human Motion Data Retrieval Based on Dynamic Time Warping Optimization Algorithm

随着大量三维人体运动数据库的建立,使得在数据库中实现基于内容的三维人体运动检索面临着诸多困难,文中提出一种分阶段的动态时间变形(DTW)优化算法的人体运动数据检索技术,可有效检索出逻辑上相似的运动.该算法首先对齐两个运动序列的坐标位置,基于窗口距离构造距离矩阵.其次采用基于全局和局部约束的DTW优化算法进行相似度匹配,得到两个运动间的对应关系.最后通过归一化相似度和DTW平均距离分阶段判断运动的相似性.实验结果表明,分阶段的DTW优化算法在提高效率的同时对长度不等的运动能取得较好的检索结果.

With the emergence of many large-scale three-dimensional human motion databases, content-based retrieval of 3D human motion faces many difficulties. A human motion data retrieval technology based on improved dynamic time warping optimization algorithm is proposed, by which logically similar motions can be found effectively. Firstly, the coordinates of two motion sequences are aligned and a distance matrix based on window of frames is constructed. Then, using an optimization algorithm based on global and local constraints, a similarity matching is processed to describe corresponding relationship between two motions. Finally, similar motions are retrieved by two-phase approach with normalization similarity and DTW average distance. The experimental results show that by two-phase DTW optimization approach, better retrieval results for motions which are not aligned in time axis are obtained and the efficiency is improved.