To improve the robustness of the tracker

a novel algorithm

the Diverse AdaBoost SVM Tracking(DABSVT) method

is proposed for target tracking in infrared imagery. The tracker trains one Support Vector Machine(SVM) classifier per frame. All of the classifiers are combined into an ensemble classifier using AdaBoost. By proper parameter adjusting strategies

a set of effective SVM classifiers with moderate accuracy are obtained. The ensemble classifier is used to distinguish the target from the background in the next frame and produce a confidencemap. The peak of themap

which is given bymean shift

is thought as the new position of the target. To cope with the changes in features of both foreground and background

the component classifier can be discarded or added at any time. The experiments performed on several sequences showed the robustness of the proposed method.