A Compensatory Algorithm for High-Speed Visual Object Tracking Based on Markov Chain

被引：0

作者：

Song, Zhenzhong ^{[1
]}

Peng, Xiaoqing ^{[2
]}

He, Huijun ^{[1
]}

Wang, Gaoang ^{[3
]}

机构：

[1] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China

[2] Fudan Univ, Fudan Univ Lib, Shanghai, Peoples R China

[3] Fudan Univ, China Dept Elect Engn, Shanghai, Peoples R China

来源：

ADVANCES IN ELECTRONIC COMMERCE, WEB APPLICATION AND COMMUNICATION, VOL 2 | 2012年 / 149卷

关键词：

Visual tracking; Markov Chain; machine learning; online algorithms;

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：

In order to effectively track high-speed objects in real time without any other instrumental methods but only based on computer vision, we propose an algorithm on camera control part by utilizing a history learning model in which a one-dimension Markov Transition Matrix synthesizes some historical information efficiently. The algorithm provides serials of conventional algorithms an effectively compensation when they perform worse in an interactive, high-speed circumstance, even lose the targets. Experimental results show that the new method is more stable and robust.

引用

页码：497 / +

页数：2

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