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	Adaboost目标跟踪算法
	贾静平 ; 张飞舟 ; 柴艳妹 ; JIA Jing-Ping ; ZHANG Fei-Zhou ; CHAI Yan-Mei
	2010-06-09 ; 2010-06-09
关键词	Adaboost分类器 图像跟踪 序列图像分析 Adaboost Classifier,Image Tracking,Image Sequence Analysis TP391.41
其他题名	Adaboost Object Tracking Algorithm
中文摘要	从两类模式分类技术的角度看待视频序列中的目标跟踪问题,提出一种基于Adaboost学习技术的跟踪算法.首先利用像素RGB分量的整数系数的线性组合生成备选目标特征,以类间方差比为标准选出较好的特征来设计若干弱分类器,进而构造出一个强分类器.然后对于每帧输入图像,按照该强分类器对各像素进行分类,生成目标概率分布图.再通过结合信任域算法和尺度空间理论实现对分布图中的灰度块定位,从而完成目标跟踪.此外还通过在线集成新的弱分类器和对已有弱分类器权值的更新,提高算法对光照变化等因素引起的特征变化的适应能力.在大量真实序列图像上进行实验验证,并与现有算法进行比较,结果表明该算法不但能更好地应对目标特征变化,在存在干扰的背景中稳定跟踪目标,也能更准确地描述目标大小,显著提高跟踪算法精度.; An Adaboost based algorithm for object tracking in image sequences is proposed.In this algorithm,tracking is considered as a binary classification problem.Firstly,the linear combination of R,G,and B with integer coefficients is used to generate the candidate features.Features are selected for the design of weak classifiers according to the two-class variance ratio.Then,a strong classifier is built on the weak classifiers.For each incoming frame,a likelihood image of the object is created according to the classification results of pixels by the strong classifier.The trust region method and the scale space theory are employed to locate the blobs in the likelihood image,and thus the object tracking is fulfilled.The changes ofillumination often cause the changes of features.The adaptability of the proposed algorithm is improved by online integration of new weak classifiers and automated weights update of the used ones.Based on the tracking results of sequence examples,the proposed algorithm can adapt to feature changes,track object in cluttered background and describe the object accurately with better tracking precision.; 国家863计划资助项目(No.2009AA12Z352)
语种	中文 ; 中文
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/55270]
专题	清华大学
推荐引用方式 GB/T 7714	贾静平,张飞舟,柴艳妹,等. Adaboost目标跟踪算法[J],2010, 2010.
APA	贾静平,张飞舟,柴艳妹,JIA Jing-Ping,ZHANG Fei-Zhou,&CHAI Yan-Mei.(2010).Adaboost目标跟踪算法..
MLA	贾静平,et al."Adaboost目标跟踪算法".(2010).

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