基于双隐变量空间局部粒子搜索的人体运动形态估计

摘要

该文提出一种双隐变量空间局部粒子搜索(DLVSLPS)算法，可以从多视角图像序列的轮廓特征较准确地估计出3维人体运动形态序列。该算法用高斯过程动态模型(GPDM)降维建立双隐变量空间和低维隐变量数据到高维数据的映射关系后，然后对双隐变量空间使用近邻权重先验条件搜索(NWPCS)，实现局部低维粒子搜索来生成较优高维数据，从而估计相应帧的3维人体运动形态，解决传统粒子滤波算法直接在高维数据空间采样较难获取有效正确数据进行估计的问题。经仿真实验验证，所提出的算法比传统粒子滤波算法在实现多视角非连续帧估计，克服轮廓图像数据歧义，减小估计误差有明显优势。%A novel algorithm called Dual Latent Variable Spaces Local Particle Search (DLVSLPS) is proposed. It can estimate the 3D human motion sequence from silhouettes of multi-view image sequence more accurately. Gaussian Process Dynamical Models (GPDM) is used to reduce the dimension to build the dual latent variable spaces and the mapping from low dimensional latent variable data to high dimensional data. Then, the low dimensional particles are searched in these spaces by the method called Neighbor Weight Prior Condition Search (NWPCS). The better high dimensional data are generated from the mapping to estimate the 3D human motion of the corresponding frame. The proposed algorithm aims to solve the problem of traditional particle filters. The problem is that sampling in high dimensional data space can not get the valid and correct data to estimate the 3D human motion. The simulating experiments show the proposed algorithm has better performance than the traditional particle filters. The better performance includes the multi-view and discontinuous frame estimation, overcoming the silhouette ambiguity and reducing the estimation error.