【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.更多还原

【Abstract】 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.更多还原