一种改进型的粒子滤波器

An Improved Particle Filter

针对复杂背景条件下图像序列中跟踪运动目标的问题,提出一种改进的粒子滤波图像跟踪算法,该算法利用遗传算法的研究成果,采用选择、交叉、变异等步骤实现对粒子的重采样,解决了粒子滤波器所面临的粒子退化和匮乏问题。由于该算法利用了遗传算法的全局寻优特性,因此该算法具有较强的稳健性。同时,粒子滤波可实现非线性非高斯状态空间模型的最优估计,将该粒子滤波用于目标跟踪,具有较好的过遮挡能力。实验结果表明,该算法状态估计性能好,能够很好地实现复杂图像序列中的目标跟踪。

Focusing on the problem of tracking moving object in image sequences with complex background, an improved particle- filtering tracking algorithm is proposed. Using the researches of the Genetic Algorithm ,the algorithm accomplishes the resample processes from selection, crossover and mutation processes to tackle the finite particle problem by re-defining or re-supplying impoverished particles during filter iteration. Because of the global optimization characteristic of the Genetic Algorithm, the algorithm is very robust. What＇ s more,the particle filter could solve non-linear and non-Gaussian state estimation and cope with partial occlusions then recovering the tracks after temporary loss. The experimental results show that the new algorithm could accomplish the state estimation and the object tracking in complex image sequences.