电子学报 ›› 2017, Vol. 45 ›› Issue (12): 3060-3069.DOI: 10.3969/j.issn.0372-2112.2017.12.032
李万益1,2, 孙季丰1
收稿日期:
2015-01-28
修回日期:
2016-11-30
出版日期:
2017-12-25
通讯作者:
作者简介:
基金资助:
LI Wan-yi1,2, SUN Ji-feng1
Received:
2015-01-28
Revised:
2016-11-30
Online:
2017-12-25
Published:
2017-12-25
Supported by:
摘要: 为了从多视角轮廓图像估计出含空间位置信息的三维人体运动形态,该文提出高斯增量降维与流形Boltzmann优化(GIDRMBO)算法.该算法把表示三维人体运动形态的高维数据分成表示空间位置信息和姿态信息两段子向量后,用高斯增量降维模型(GIDRM)分别对其样本进行降维,建立相应的低维空间及映射关系,然后在相应的低维空间使用流形Boltzmann优化算法来对轮廓匹配目标函数进行优化,从而实现估计.其中,所提算法分别利用了两段子向量样本的低维数据作为先验信息,可较好的避免陷入局部最优区域进行搜索,最终生成与各视角原始运动图像匹配且含空间位置信息的三维人体运动形态.经仿真实验验证,所提算法与常用粒子滤波算法相比,其估计误差小,并且还能起到消除轮廓数据歧义和克服短时遮挡的作用.
中图分类号:
李万益, 孙季丰. 基于高斯增量降维与流形Boltzmann优化的人体运动形态估计[J]. 电子学报, 2017, 45(12): 3060-3069.
LI Wan-yi, SUN Ji-feng . Human Motion Estimation Based on Gaussion Incremental Dimension Reduction and Manifold Boltzmann Optimization[J]. Acta Electronica Sinica, 2017, 45(12): 3060-3069.
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