为了提高目标跟踪的鲁棒性，提出了一种新的用于红外目标跟踪的DABSVT算法。该算法首先把目标跟踪转化为目标和背景的两类分类问题，然后将根据每一帧的正负样本训练的支持向量机（SVM）作为分量分类器，并通过恰当的参数调整策略，利用AdaBoost算法把这些分量分类器组合成一个总体分类器；接着利用该总体分类器来区分下一帧中的目标和背景，并得到置信图；最后通过均值漂移算法找到置信图的峰值，得到目标的新位置。该新位置不仅与目标和背景的变化相适应，而且分量分类器可以随时加入或丢掉。实验结果显示，该方法是鲁棒的。

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.