Robust object tracking based on adaptive and incremental subspace learning

Xiao Min Tong, Yan Ning Zhang, Tao Yang

科研成果: 期刊稿件文章同行评审

6 引用 (Scopus)

摘要

The traditional target tracking algorithm usually trains the template with detected samples and updates the template at a fixed frequency. This close-loop mechanism lacks feedback and often makes it impossible to track targets robustly when target appearance or illumination changes. Besides, it can not recover from tracking failure easily. Therefore, we propose a feedback-loop tracking framework by bringing in the tracking state judgement. In this framework, the tracking state judgement works as the basis of the following template updating. According to the tracking state judgement, we can choose suitable samples to update the template at appropriate time so as to track targets continuously. Experimental results show that our method can get the current template immediately and correctly due to the tracking state judgement and decision mechanism. We can upate the template at an adaptive frequency and meanwhile track targets correctly even in the case of target appearance or illumination changing.

源语言英语
页(从-至)1483-1494
页数12
期刊Zidonghua Xuebao/Acta Automatica Sinica
37
12
DOI
出版状态已出版 - 12月 2011

指纹

探究 'Robust object tracking based on adaptive and incremental subspace learning' 的科研主题。它们共同构成独一无二的指纹。

引用此