Abstract：To solve the problem that traditional low-level segmentation methods can hardly achieve suitable segmentation for motion data due to high dimensionality, a two-phase low-level motion data segmentation method was proposed based on relative position. The positions of end joints relative to root joint were utilized as the features and were later processed to remove noise by Butterworth filter. According to zero-crossing and thresholding methods, the segment points for each dimension were located in three dimensions of end joint and synthesized to get segment points for end joint. Those segment points of all end joints were merged to derive the segment points for whole body. The 12 941 frames of motions from CMU Mocap database were explored to evaluate the proposed method. The results show that the proposed method is effective for segmenting motion capture data. Compared with the 2 velocity-based segmentation methods, the proposed method can lead to better recall and precision ratio.
杨洋, 詹永照, 王新宇. 基于相对位置的2阶段低级动作分割方法[J]. 江苏大学学报（自然科学版）, 2017, 38(2): 186-191.
YANG Yang, ZHAN Yong-Zhao, WANG Xin-Yu. A two-phase low-level motion data segmentation method based on relative position[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2017, 38(2): 186-191.
2017, Vol. 38 
