摘要
针对均值漂移跟踪算法框架不足以对目标帧间运动过大及快速尺度变化进行有效地处理,且单个图像特征对环境适应性较差.提出了一种特征自适应选择方法,通过分析目标与背景的特征区分度来选择出最有效的特征.将金字塔自适应分解和均值漂移跟踪结合,提出了金字塔均值漂移跟踪方法.采用背景加权直方图描述目标模板模型,核函数加权直方图描述候选目标模型,由粗到精定位目标,并给出了目标尺度自适应更新方法.多个视频序列的实验结果表明:本文方法能够有效处理目标快速运动、尺度变化、摄像机运动、局部遮挡等情况,实现复杂场景下的目标跟踪.
Aiming at shortages of the mean shift tracking algorithm in dealing with the cases that the displacements of target between two successive frames are relatively large and the scales of target change quickly,and the poor adaptability of single feature to the changeable circumstance,an adaptive feature selection method is presented,to determine the most effective feature by analyzing the discriminative value of target and background.By representing the target model and the target candidate in terms of background weighted histogram and kernel weighted histogram respectively,and using the pyramid analysis technique,the pyramid mean shift tracking method is proposed to localize target via a coarse-to-fine way.Furthermore,a scale update mechanism is presented.Experimental results on various videos show that the proposed method can successfully cope with the cases such as high-speed moving target,scale variations,camera motion,partial occlusions,etc.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2011年第1期154-160,共7页
Acta Photonica Sinica
关键词
目标跟踪
金字塔均值漂移
特征自适应选择
Target tracking
Pyramid Mean Shift（PMS）
Adaptive feature selection
