Small target tracking based on histogram interpolation mean shift

被引：3
|
作者：
Chen J.-J. [1 ]
An G.-C. [2 ]
Zhang S.-F. [1 ]
Wu Z.-Y. [1 ]
机构：
[1] School of Information Science and Engineering, Southeast University
[2] Intelligence Engineering Lab., Institute of Software Chinese Academy of Sciences
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2010年 / 32卷 / 09期
关键词：
Histogram interpolation; Mean shift; Parzen window; Similarity measure; Small target tracking;
D O I：
10.3724/SP.J.1146.2009.01245
中图分类号：
学科分类号：
摘要：
Small scale target tracking is one of the primary difficulties in visual tracking. Two major problems in mean shift small target tracking algorithm are presented in this paper, namely tracking interrupt and target losing. To tackle these problems, the Parzen windows density estimation method is modified to interpolate the histogram of the target candidate. The Kullback-Leibler distance is employed as a new similarity measure function of the target model and the target candidate. And its corresponding weight computation and new location expressions are derived. On the basis of these works, a new mean shift algorithm is proposed for small target tracking. Several tracking experiments for real world video sequences show that the proposed algorithm can track the target successively and accurately. It can successfully track very small targets with only 6 × 12 pixels.
引用
收藏
页码：2119 / 2125
页数：6
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