层级潜变量空间中的三维人手跟踪方法  

Tracking 3D Hand in Hierarchical Latent Variable Space

作　　者：韩磊[1] 梁玮[1] 贾云得[1] 

机构地区：[1]北京理工大学计算机科学技术学院智能信息技术北京市重点实验室,北京100081

出　　处：《计算机辅助设计与图形学学报》2009年第5期650-656,共7页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(60473049);国家"八六三"高技术研究发展计划(2006AA01Z120)

摘　　要：针对人手状态空间维数过高的问题,提出一种基于层级流形学习的三维人手跟踪方法.将人手状态空间划分成多个人手部分状态空间,采用层级高斯过程潜变量模型得到更能反映人手运动本质的树状低维流形空间,降低了粒子滤波器有效跟踪人手所需的粒子数量;使用径向基函数插值方法构建低维流形空间到图像空间的非线性映射,将低维粒子直接映射到图像空间中观测.实验结果表明,该方法可以鲁棒地跟踪关节人手.Since the dimensionality of hands state space is too high, we employ a hierarchical Gaussian process latent variable model （GPLVM） to simultaneously learn the hierarchical latent space of hands motion and the nonlinear mapping from the hierarchical latent space to the state space of human hands. Nonlinear mappings from the hierarchical latent space to the space of hand images are constructed using radial basis function interpolation method. With these mappings, particles can be projected into hand images and measured in the images space directly. Then particle filters with fewer particles are used to track hands in the learned hierarchical low-dimensional space. Experimental results show that our proposed method can track articulated hand robustly and efficiently.

关 键 词：三维人手跟踪 层级流形学习 高斯过程潜变量模型 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]