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2007
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Object Classification in Visual Surveillance Using Adaboost

10 years 5 months ago
Object Classification in Visual Surveillance Using Adaboost
In this paper, we present a method of object classification within the context of Visual Surveillance. Our goal is the classification of tracked objects into one of the two classes: people and cars. Using training data comprised of trajectories tracked from our car-park, a weighted ensemble of Adaboost classifiers is developed. Each ensemble is representative of a particular feature, evaluated and normalised by its significance. Classification is performed using the sub-optimal hyperplane derived by selection of the N-best performing feature ensembles. The resulting performance is compared to a similar Adaboost classifier, trained using a single ensemble over all dimensions.
John-Paul Renno, Dimitrios Makris, Graeme A. Jones
Added 14 Aug 2010
Updated 14 Aug 2010
Type Conference
Year 2007
Where CVPR
Authors John-Paul Renno, Dimitrios Makris, Graeme A. Jones
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