特征融合和聚类核函数平滑采样优化的粒子滤波目标跟踪方法  

Optimal Particle Filter Object Tracking Algorithm Based on Features Fusion and Clustering Kernel Function Smooth Sampling

作　　者：李科[1] 徐克虎[1] 

机构地区：[1]装甲兵工程学院控制工程系,北京100072

出　　处：《计算机科学》2012年第4期210-213,共4页Computer Science

基　　金：总装备部重点科研项目(2007SC02);国防预研基金项目(9140A09050708JB3503)资助

摘　　要：针对复杂场景下的目标跟踪问题,提出了一种改进的粒子滤波目标跟踪方法。利用背景加权后的联合直方图描述目标灰度和梯度特征信息,在粒子滤波算法的框架下,设计了一种自适应特征融合观测模型来适应场景的不断变化;同时针对传统粒子滤波算法存在的粒子退化问题,提出了一种基于聚类核函数平滑采样的方法。理论仿真和实际场景的实验结果表明,该算法适应性更强,精度更高,能有效跟踪复杂场景下的运动目标。An improved particle filter object tracking algorithm was proposed to solve object tracking problems in complex scene.This paper used united histogram to describe target grayscale and gradient direction features imformation,and designed a self-adaptive features fusion observation model to adapt the changing scene.To solve particles degeneracy problem of basic particle filter,a resampling method based on clustering kernel function smooth was proposed.The experimental results based on simulation and the actual scenes show that this algorithm is more adaptable and possesses higher accuracy,can track the moving object in complex scene effectively.

关 键 词：目标跟踪 特征融合 粒子滤波 重采样 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]