提出一种粒子滤波方法中粒子样本采样方法,旨在用较少的粒子数描述高维状态变量的后验概率分布.首先,研究特定人机交互条件下操作者的认知心理特点和人手运动特点,在此基础上提出了状态变量微观结构的基本概念;然后,进一步探讨描述微观结构的一般方法;最后,提出了基于状态变量微观结构的粒子采样方法和改进的粒子滤波跟踪算法.状态变量的微观结构为采样算法的设计提供了一种统一、高效的数学模型,以此为基础的采样算法可以有效避免对质量比较差的粒子样本进行大量采样.为了验证算法的有效性和性能,进行了大量实验,结果表明,与传统的粒子滤波方法相比,采用少量的粒子样本就可以达到较高的跟踪精度.

Aiming at describing post probability distribution of the state with high dimensionality by using of a small amount of particles, a sampling in particle filtering for human hand 3D tracking is put forward in this paper. First of all, with the specific human-computer conditions, both cognitive psychology features of operators and motion features of the operators' hands are studied, upon which a novel concept, microstructure of state variable, is proposed. Then, a general method to describe the microstructure of state variable is further discussed. At last, a sampling algorithm based on the microstructure of state variable is put forward. The microstructure of state variable provide an unite and efficient mathematic model upon which the sampling algorithm may avoid sampling mass particles with poor quality. In order to validate validity and performance of sampling algorithm, a great deal of experiments are completed, and just fewer particles, compared with conventional particle filtering, may achieve better tracking precision.

Supported by the National Natural Science Foundation of China under Grant No.60773109 (国家自然科学基金); the Natural Science Foundation of Shandong Province of China under Grant No.Y2007G39 (山东省自然科学基金); the Key Project of Natural Science Foundation of Shandong Province of China under Grant No.2006G03 (山东省自然科学重点基金); the Natural Science Foundation of Shandong Province of China for Distinguished Young Scholars under Grant No.JQ200820 (山东省自然科学杰出青年基金); the Science and Technology Plan of Shandong Province Education Department of China under Grant No.J07YJ18 (山东省教育厅科技计划); the Ph.D. Scientific Research Foundation of University of Ji'nan of China under Grant No.XBS0705 (济南大学博士科研启动基金)