中文摘要：

传统的粒子滤波算法通常使用大量粒子表示目标状态的后验概率密度函数，算法的计算量较大，跟踪的实时性较差，且无法对快速、遮挡目标进行准确跟踪．针对以上问题，提出了一种嵌入MeanShift（均值偏移）的粒子滤波算法，该方法充分利用了MeanShift聚类作用，使得粒子分布更加合理，不但提高了粒子的多样性，而且有效减少了描述目标状态的粒子数目．实验结果表明，改进的目标跟踪算法具有较强的鲁棒性和较好的实时性．

英文摘要：

Traditional particle filter algorithm needs a large number of particles to show posteriori probability density function of object state, the calculation of this algorithm is large, and the real-time of tracking is poor, so it is hard to track fast and sheltered object accurately. Considering above problems, this paper proposes a new algorithm that is inserting Mean Shift into particle filter algorithm, this method can make full use of clustering effect of Mean shift to make particles distributed more reasonably, which not only improves the diversity of the particles but also greatly reduces the number of particles used to describe object state. The experimental results show that the improved algorithm has stronger robustness and better real-time performance.