Abstract：To solve the problem that traditional low-level temporal segmentation methods could hardly work out a suitable segmentation for motion capture data with high dimensionality, a low-level temporal segmentation algorithm was proposed based on weighted confidence. The confidence as segment boundary of each time point was calculated in each dimension, and the confidence values of different dimensions were incorporated by zero-crossing strength. The whole body segment boundaries were derived by pruning the local maximum and determined by an optimum empirical threshold. The optimum threshold was obtained by testing several sets of data. The experiments were completed based on the data from motion capture database of Carnegie Mellon University. The results show that the proposed method is effective for segment motion capture data, and under the optimum threshold condition, the method is far better than the method based on curvature or whole body rate.
杨洋, 詹永照, 王新宇. 基于加权置信度的运动捕捉数据低级时域分割算法[J]. 江苏大学学报（自然科学版）, 2015, 36(3): 312-318.
Yang Yang, Zhan Yongzhao, Wang Xinyu. Low-level temporal segmentation of motion capture data based on weighted confidence[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2015, 36(3): 312-318.
2015, Vol. 36 
