Object Classification in Visual Surveillance Using Adaboost

John-Paul Renno, Dimitrios Makris, Graeme A. Jones

Abstract: 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 hyper-plane 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.