2017 14th Conference on Computer and Robot Vision (CRV) 2017
DOI: 10.1109/crv.2017.35
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Efficient Version-Space Reduction for Visual Tracking

Abstract: Discrminative trackers, employ a classification approach to separate the target from its background. To cope with variations of the target shape and appearance, the classifier is updated online with different samples of the target and the background. Sample selection, labeling and updating the classifier is prone to various sources of errors that drift the tracker. We introduce the use of an efficient version space shrinking strategy to reduce the labeling errors and enhance its sampling strategy by measuring … Show more

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Cited by 1 publication
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References 43 publications
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“…Active Collaboration of Classifiers for Visual Tracking http://dx.doi.org/10.5772/intechopen.74199 knowledge of the committee. In such frameworks the labeling process is performed by leveraging a group of classifiers with different views [45,56,80], subsets of training data [57,81], or memories [57,82].…”
Section: Active Ensemble Co-trackingmentioning
confidence: 99%
“…Active Collaboration of Classifiers for Visual Tracking http://dx.doi.org/10.5772/intechopen.74199 knowledge of the committee. In such frameworks the labeling process is performed by leveraging a group of classifiers with different views [45,56,80], subsets of training data [57,81], or memories [57,82].…”
Section: Active Ensemble Co-trackingmentioning
confidence: 99%