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Discrminative trackers, employ a classification approach to separate thetarget from its background. To cope with variations of the target shape andappearance, the classifier is updated online with different samples of thetarget and the background. Sample selection, labeling and updating theclassifier is prone to various sources of errors that drift the tracker. Weintroduce the use of an efficient version space shrinking strategy to reducethe labeling errors and enhance its sampling strategy by measuring theuncertainty of the tracker about the samples. The proposed tracker, utilize anensemble of classifiers that represents different hypotheses about the target,diversify them using boosting to provide a larger and more consistent coverageof the version-space and tune the classifiers' weights in voting. The proposedsystem adjusts the model update rate by promoting the co-training of theshort-memory ensemble with a long-memory oracle. The proposed trackeroutperformed state-of-the-art trackers on different sequences bearing varioustracking challenges.

Efficient Version-Space Reduction for Visual Tracking