Novel algorithm based on nonnegative least-square estimation for visual object tracking

被引：0

作者：

Liu, Rongli ^{[1
]}

Jing, Zhongliang ^{[1
]}

Pan, Han ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Sch Aeronaut & Astronaut, Shanghai 200240, Peoples R China

来源：

OPTICAL ENGINEERING | 2012年 / 51卷 / 03期

基金：

中国国家自然科学基金;

关键词：

least square estimation; particle filter; nonnegativity;

D O I：

10.1117/1.OE.51.3.037201

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

We present a novel algorithm based on nonnegative least square (LS) estimation for visual object tracking. Most existing algorithms, such as template matching, only consider each candidate separately. We offer another, new perspective. The template model is approximated by the linear combination of candidates in the space spanned by the feature vectors associated with particles. The coefficient of the linear combination is achieved through solving a nonnegative LS problem, and then it is used to evaluate the similarity between the template and candidates. Experimental results demonstrate that the proposed algorithm has better tracking accuracy than the compared algorithms and strong robustness against noise. (c) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.OE.51.3.037201]

引用

页数：7

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