Online Learning of Linear Predictors for Real-Time Tracking

被引：0

作者：

Holzer, Stefan ^{[1
]}

Pollefeys, Marc ^{[2
]}

Ilic, Slobodan ^{[1
]}

Tan, David Joseph ^{[1
]}

Navab, Nassir ^{[1
]}

机构：

[1] Tech Univ Munich, Dept Comp Sci, Boltzmannstr 3, D-85748 Garching, Germany

[2] Swiss Fed Inst Technol, Dept Comp Sci, CH-8092 Zurich, Switzerland

来源：

COMPUTER VISION - ECCV 2012, PT I | 2012年 / 7572卷

关键词：

template tracking; template learning; linear predictors;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Although fast and reliable, real-time template tracking using linear predictors requires a long training time. The lack of the ability to learn new templates online prevents their use in applications that require fast learning. This especially holds for applications where the scene is not known a priori and multiple templates have to be added online. So far, linear predictors had to be either learned offline [1] or in an iterative manner by starting with a small sized template and growing it over time [2]. In this paper, we propose a fast and simple reformulation of the learning procedure that allows learning new linear predictors online.

引用

页码：470 / 483

页数：14