Proceedings Paper
Mutual information for enhanced feature selection in visual tracking| Format | Member Price | Non-Member Price |
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Paper Abstract
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.
Paper Details
Date Published: 14 May 2015
PDF: 11 pages
Proc. SPIE 9476, Automatic Target Recognition XXV, 947603 (14 May 2015); doi: 10.1117/12.2176556
Published in SPIE Proceedings Vol. 9476:
Automatic Target Recognition XXV
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)
PDF: 11 pages
Proc. SPIE 9476, Automatic Target Recognition XXV, 947603 (14 May 2015); doi: 10.1117/12.2176556
Show Author Affiliations
Victor Stamatescu, Univ. of South Australia (Australia)
Sebastien Wong, Defence Science and Technology Organisation (Australia)
David Kearney, Univ. of South Australia (Australia)
Sebastien Wong, Defence Science and Technology Organisation (Australia)
David Kearney, Univ. of South Australia (Australia)
Ivan Lee, Univ. of South Australia (Australia)
Anthony Milton, Univ. of South Australia (Australia)
Anthony Milton, Univ. of South Australia (Australia)
Published in SPIE Proceedings Vol. 9476:
Automatic Target Recognition XXV
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)
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