Online selection of discriminative tracking features

This paper presents an online feature selection mechanism for evaluating multiple features while tracking and adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking the ob...

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Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence. - 1979. - 27(2005), 10 vom: 10. Okt., Seite 1631-43
1. Verfasser: Collins, Robert T (VerfasserIn)
Weitere Verfasser: Liu, Yanxi, Leordeanu, Marius
Format: Aufsatz
Sprache:English
Veröffentlicht: 2005
Zugriff auf das übergeordnete Werk:IEEE transactions on pattern analysis and machine intelligence
Schlagworte:Evaluation Study Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.
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245 1 0 |a Online selection of discriminative tracking features 
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520 |a This paper presents an online feature selection mechanism for evaluating multiple features while tracking and adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking the object. Given a set of seed features, we compute log likelihood ratios of class conditional sample densities from object and background to form a new set of candidate features tailored to the local object/background discrimination task. The two-class variance ratio is used to rank these new features according to how well they separate sample distributions of object and background pixels. This feature evaluation mechanism is embedded in a mean-shift tracking system that adaptively selects the top-ranked discriminative features for tracking. Examples are presented that demonstrate how this method adapts to changing appearances of both tracked object and scene background. We note susceptibility of the variance ratio feature selection method to distraction by spatially correlated background clutter and develop an additional approach that seeks to minimize the likelihood of distraction 
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650 4 |a Journal Article 
650 4 |a Research Support, N.I.H., Extramural 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
650 4 |a Research Support, U.S. Gov't, P.H.S. 
700 1 |a Liu, Yanxi  |e verfasserin  |4 aut 
700 1 |a Leordeanu, Marius  |e verfasserin  |4 aut 
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