基于椭圆区域协方差描述子和卡尔曼粒子滤波的鲁棒视觉跟踪方法  

Robust visual tracking method based on elliptical region covariance descriptor and Kalman particle filter

作　　者：朱明清[1] 陈宗海[1] 

机构地区：[1]中国科学技术大学自动化系,合肥230027

出　　处：《控制与决策》2011年第5期721-726,共6页Control and Decision

基　　金：高等学校博士学科点专项科研基金项目(20093402110014);国家863计划项目(2009AA11A113)

摘　　要：针对传统的或概率性的协方差跟踪中采用的矩形区域协方差描述子不能精确反映目标的非刚性形变的问题,提出椭圆区域协方差描述子,将各种不同类型的特征融入统一的模型中,实现了基于多特征的目标跟踪,并将其与卡尔曼粒子滤波相结合,提出一种鲁棒的视觉目标跟踪方法.实验结果表明,该方法对光线变化、噪声干扰、部分和完全遮挡、目标的尺度和角度的变化以及目标的快速运动都具有较好的鲁棒性.Rectangular region covariance descriptor used in traditional and probabilistic covariance tracking can not reflect object’s non-rigid deformation precisely.Therefore,a covariance descriptor,elliptical region covariance descriptor,is proposed,which enables efficient fusion of a diversity of features and modalities into a unified feature model of multi-cue integration for visual tracking.Further combining this descriptor with Kalman particle filter,a robust visual tracking method is proposed.Experimental results show the proposed methed has robustness to illumination changes,noise disturbance,partial and fully occlusion,scale variation,rotation and abrupt object movement.

关 键 词：椭圆区域协方差描述子 协方差跟踪 卡尔曼粒子滤波 视觉跟踪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]