基于智能优化粒子滤波算法的人体运动目标跟踪

Combining Particle Filter with Local Optimization for Target Tracking

粒子滤波算法应用于目标跟踪时,存在样本贫化和计算量大的问题,提出了一种基于智能优化粒子滤波算法。利用粒子群算法良好的局部寻优和全局寻优能力对重采样之后的粒子集进行操作,使粒子可以智能地合作起来,减轻样本贫化。实验结果表明,该算法实时性强,提高目标状态的估计精度,缩短了计算时间,其滤波性能优于常规粒子滤波算法。

The Particle Filter （PF） has been applied in target tracking successfully,but it exist the problems of sample depletion and large calculated amount. To overcome the drawback,an efficient PF algorithm based on intelligent optimization algorithms is proposed. It use the good local and global optimization ability of PSO to make particles in re-sampling particle sets co-operate with each other intelligently and alleviate the sample depletion. The experimental result shows that the presented algorithm improves the accuracy of target state estimation and reduces the computing time,and also has a better practicability with higher real-time performance.