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Boosting adaptive linear weak classifiers for online learning and tracking

Parag, Toufiq; Porikli, Fatih; Elgammal, Ahmed


Online boosting methods have recently been used successfully for tracking, background subtraction etc. Conventional online boosting algorithms emphasize on interchanging new weak classifiers/features to adapt with the change over time. We are proposing a new online boosting algorithm where the form of the weak classifiers themselves are modified to cope with scene changes. Instead of replacement, the parameters of the weak classifiers are altered in accordance with the new data subset presented...[Show more]

CollectionsANU Research Publications
Date published: 2008
Type: Conference paper
Source: Proceedings of CVPR 2008
DOI: 10.1109/CVPR.2008.4587556


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