%0 Journal Article
%T Mean Shift based object tracking with similarity and affine transformations<br>基于Mean Shift的相似性变换和仿射变换目标跟踪算法
%A Li Peihua
%A Xiao Lijuan
%A <br>李培华
%A 肖莉娟
%J 中国图象图形学报
%D 2011
%I 
%X Traditional Mean Shift (MS) algorithm can only follow objects with translation and scale change, and fails to handle objects with similarity transformation or complex affine transformation. To address this problem, the paper presents two improved algorithms. The first one focuses on the affine motion. According to the theory of Singular Value Decomposition, the affine matrix can be factored into product of two rotation matrixes and one diagonal matrix, based on which a new candidate model is proposed. With Bhattacharyya coefficient as a similarity function, the object tracking is formulated as an optimization problem, and the corresponding MS algorithm can be derived by calculating the first derivative of the similarity function with respect to affine parameters and setting them to be zero. Furthermore, a new candidate model is proposed that handles similarity transformation, and the corresponding MS algorithm can be obtained that estimates the translation vector and rotation angle. Experimental results show that, the proposed algorithms can track objects with similarity or affine tranformations, and have better tracking performance than the traditional one.
%K Object tracking
%K Mean Shift algorithm
%K affine transformation
%K similarity transformation<br>目标跟踪
%K Mean
%K Shift算法
%K 仿射变换
%K 相似性变换
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=CC98876BCF39F7457BCFEB146490668D&yid=9377ED8094509821&vid=7801E6FC5AE9020C&iid=0B39A22176CE99FB&sid=B799C1769FCACDC8&eid=4FE459D71E3BF8EB&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=16