中文摘要：

针对传统粒子滤波的建议分布没有利用到当前观测信息的不足，本文提出了一种基于运动检测以改进建议分布的粒子滤波跟踪方法。该方法利用系统的状态转移密度分布，结合目标当前时刻的运动信息共同决定目标的先验分布。首先从一阶自回归的状态转移模型中生成部分粒子，然后采用单高斯背景建模进行局部运动检测，在检测到的运动区域中采样其余粒子，由此得到粒子的先验分布。用该方法分别对动态背景和存在完全遮挡情况下的运动目标进行跟踪，实验结果表明该方法有较高的跟踪精度和较强的稳定性。

英文摘要：

The performance of a particle filter is strongly influenced by the choice of proposal distribution. In order to improve the performance of particle filter for target tracking, a particle filter tracking method based on motion detection is proposed to improve the proposal distribution. A new proposal distribution, which integrates the motion information of the current frame with the prior distribution, is developed. A part of the particles is sampled from the system transition density, and the others from the motion region detected by using the Gauss background modeling. Thus, the prior distribution of particles is determined by both the system transition density and the observations. The experiments show that the method is very effective under the moving background and the occlusion circumstances.