Robust Object Tracking Based on Adaptive and Incremental Subspace Learning
基于增量子空间自适应决策的目标跟踪

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.