由于光照的变化、形变、姿势的变化，传统的粒子滤波算法会发生跟踪漂移。影响了跟踪算法的准确性和稳定性。发生漂移的主要原因是目标函数不够平滑，从而使得更新的状态存在较大误差。本文提出了一种新型的层叠式粒子滤波算法，该方法在离散域提取基于核的直方图作为目标模型，通过改进传统的粒子滤波算法，实现对目标由粗到细的跟踪。在公开测试集上与当前主流的跟踪算法比较，结果表明本文提出的算法在跟踪精度上提高了20%，并取得了很好的实时性和鲁棒性。

In the framework of traditional particle filter, object is always drifted for the illumination light changing, deformation and poses changing, which affects the accuracy and robust performance of tracker. The reason is that there is not smooth in the objective function. Error will accumulate during the state updating process. A new stack particle filter is proposed to represent target model by the kernel histogram in the discrete fields. It can track the target from coarse to fine by the modified particle filter. Extensive experimental results show that the proposed algorithm performs 20% improvement in the accuracy, favorably time expedition and robustness against state-of-the-art methods.