Random patch based video tracking via boosting the relative spaces

In this paper, we propose a new visual tracking method based on the recently popular tracking-as-classification idea. We concentrate on exploring the intra-class variance of the foreground target to construct and update a classification based tracker. In our approach, foreground target is represented by a set of model patches. Different types of features are jointly used to represent those patches. Individual weak learners are trained based on each model patch's relative space. AdaBoost framework is applied to choose those weak classifiers to combine a strong classifier as the tracker for next frame. Moreover, with the new tracking result, the tracker is adjusted adaptively according to the change of scene to keep itself discriminative during the entire sequence. We demonstrate the effectiveness of our approach with comparison results on common video sequences.