%0 Journal Article
%T Lucas-Kanade tracking based on sparse representation<br>稀疏表示的Lucas-Kanade目标跟踪
%A Xu Ruyi
%A Chen Jingying
%A <br>徐如意
%A 陈靓影
%J 中国图象图形学报
%D 2013
%I 
%X In this paper, we propose a new object tracking algorithm applying sparse representation in the Lucas-Kanade image registration algorithm. The object state parameters are solved to realize precise tracking by minimizing the L1-norm of the alignment error. The object appearance is represented by the static template and the dynamic dictionary. The dynamic dictionary is obtained by updating the tracking result in each frame. The object can be rebuilt by the sparse representation of the templates in the dynamic dictionary. To deal with tracking drift caused by dictionary update, a two-stage iteration with the static template and the dynamic dictionary respectively is included in our method. Numerous experimental results show that the proposed method is quite effective to partial occlusions, appearance changes and illumination changes. Meanwhile the system is computational efficient and works in real time.
%K sparse representation
%K Lucas-Kanade image registration algorithm
%K visual tracking
%K L1-minimization<br>稀疏表示
%K LK图像配准算法
%K 视觉跟踪
%K L1范数最小化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=2C0F2EE71B46795FD5E8A328E7F5238F&yid=FF7AA908D58E97FA&vid=13553B2D12F347E8&iid=38B194292C032A66&sid=E2B9962CCD971A0D&eid=F50A8B5513721E1C&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=15