CAJ | 학술논문

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。
해문제출일충쌍은변량공간국부입자수색(DLVSLPS)산법，가이종다시각도상서렬적륜곽특정교준학지고계출3유인체운동형태서렬。해산법용고사과정동태모형(GPDM)강유건립쌍은변량공간화저유은변량수거도고유수거적영사관계후，연후대쌍은변량공간사용근린권중선험조건수색(NWPCS)，실현국부저유입자수색래생성교우고유수거，종이고계상응정적3유인체운동형태，해결전통입자려파산법직접재고유수거공간채양교난획취유효정학수거진행고계적문제。경방진실험험증，소제출적산법비전통입자려파산법재실현다시각비련속정고계，극복륜곽도상수거기의，감소고계오차유명현우세。
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.