Target Tracking in Infrared Image Sequences by Combining SVM and AdaBoost
基于SVM和AdaBoost的红外目标跟踪

To improve the robustness of the tracker,a novel algorithm,the Diverse AdaBoostSVM 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 confidence map.The peak of the map,which is given by mean 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.