特征融合与视觉目标跟踪  

Likelihood map fusion for visual object tracking

作　　者：卞志国[1] 金立左[1] 费树岷[1] 

机构地区：[1]东南大学自动化学院,南京210096

出　　处：《计算机应用研究》2010年第4期1537-1539,1543,共4页Application Research of Computers

基　　金：航空科学基金资助项目(20080169003);国家自然科学基金资助项目(60805002)

摘　　要：针对跟踪过程中各类图像特征分离背景和目标能力的变化,提出一种基于增量判别分析的特征融合算法。该算法首先计算各特征图像的似然图,然后通过增量判别分析计算各特征分类性能,得到相应权重,并在此基础上求取融合似然图,通过粒子滤波算法确定待跟踪目标状态。通过对可见光及红外成像视频序列的仿真表明,该算法对环境光照变化、视角变化以及局部遮挡等均具有一定的鲁棒性。For the ability of every kinds of features to separate the object from backgroud is dynamic during the process of tracking,this paper presented an ILDA based likelihood map fusion.Gave a tracking frame,calculated each likelihood map and obtained the weight of each through ILDA,then fused the likelihood map based on the weights.At last,acquired the state of the objecte through particle filter based on the fused likelihood.Examples based on optical and infrared frames show that the proposed tracking framework is robust to the partial occlusions,view-point and illumination variations.

关 键 词：目标跟踪 增量判别分析 特征融合 粒子滤波 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]