14 May 2015 Mutual information for enhanced feature selection in visual tracking
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In this paper we investigate the problem of fusing a set of features for a discriminative visual tracking algorithm, where good features are those that best discriminate an object from the local background. Using a principled Mutual Information approach, we introduce a novel online feature selection algorithm that preserves discriminative features while reducing redundant information. Applying this algorithm to a discriminative visual tracking system, we experimentally demonstrate improved tracking performance on standard data sets.
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Victor Stamatescu, Victor Stamatescu, Sebastien Wong, Sebastien Wong, David Kearney, David Kearney, Ivan Lee, Ivan Lee, Anthony Milton, Anthony Milton, "Mutual information for enhanced feature selection in visual tracking", Proc. SPIE 9476, Automatic Target Recognition XXV, 947603 (14 May 2015); doi: 10.1117/12.2176556; https://doi.org/10.1117/12.2176556

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