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 ISCAS OpenIR  > 软件所图书馆  > 期刊论文
 Subject: Computer Science Title: 广义均值移动跟踪算法 Alternative Title: a generalized mean shift tracking algorithm Author: 陈建军 ; 张索非 ; 安国成 ; 吴镇扬 Keyword: 均值移动 ; CAMSHIFT ; 视频目标跟踪 ; 相似性度量 Source: 中国科学:信息科学 Issued Date: 2011 Volume: 41, Issue:12, Pages:1436-1449 Indexed Type: CNKI ; CSCD Department: 东南大学信息科学与工程学院;中国科学院软件研究所人机交互技术与智能信息处理实验室;东南大学水声信号处理教育部重点实验室; Sponsorship: 教育部重点实验室开放研究基金(批准号:UASP1004)|国家自然科学基金(批准号:60672094)|国家重点基础研究发展计划(批准号:2009CB320804)|中国博士后科学基金(批准号:20100470588)资助项目 Abstract: CAMSHIFT算法和Comaniciu/Meer算法是均值移动在视频目标跟踪中最为常用的两个基本算法.本文对Bradski和Comaniciu/Meer等人的工作加以推广,给出了广义均值移动跟踪算法.论文采用一个一般形式的相似性度量函数,并推导了其相应的像素权值计算和搜索窗口位置更新公式.新算法基于搜索窗内各像素权值的零阶矩来计算更新其搜索窗口尺寸.然后证明现有的两种基本算法都可以归纳到广义均值移动跟踪算法的统一框架中.对多段视频序列的跟踪实验分析比较了统一框架中3种均值移动算法的跟踪性能. English Abstract: CAMSHIFT algorithm and Comaniciu/Meer algorithm are two fundamental frameworks of mean shift procedure for video target tracking.This paper generalizes the two well-known mean shift tracking algorithms,originally due to Bradski and Comaniciu/Meer.A new general similarity function which defines the distance between the target model and target candidate is employed to calculate the pixel weights and the target location.The target size is iteratively estimated and updated based on the zeroth order moment of the pixel weights.Then we prove that both the CAMSHIFT algorithm and the Comaniciu/Meer algorithm can be included in the generalized mean shift tracking framework.The tracking performances of three mean shift algorithms in the unified framework are shown and compared in the experimental results. Language: 中文 Citation statistics: Content Type: 期刊论文 URI: http://ir.iscas.ac.cn/handle/311060/16114 Appears in Collections: 软件所图书馆_期刊论文

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 Recommended Citation: 陈建军,张索非,安国成,等. 广义均值移动跟踪算法[J]. 中国科学:信息科学,2011-01-01,41(12):1436-1449.
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