Robust visual tracking via adaptive forest

In this paper, we address the visual object tracking problem in the so-called “Tracking-by-Detection” framework, which forms a dominating trend for target tracking recently. Combining ideas from random forest and multiple instance learning, we propose a novel online ensemble classifier selection procedure to conduct the tracking task. The proposed algorithm has been tested on several challenging image sequences. Experimental results validate the effectiveness of the proposed tracking method.