Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos

Cao, X. and Wu, C. and Yan, P. and Li, Xuelong (2011) Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos. In: UNSPECIFIED (ed.) IEEE International Conference on Image Processing. Institute of Electrical and Electronics Engineers, pp. 2421-2424. ISBN 9781457713040.

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Official URL: http://dx.doi.org/10.1109/ICIP.2011.6116132

Abstract

Visual surveillance from low-altitude airborne platforms has been widely addressed in recent years. Moving vehicle detection is an important component of such a system, which is a very challenging task due to illumination variance and scene complexity. Therefore, a boosting Histogram Orientation Gradients (boosting HOG) feature is proposed in this paper. This feature is not sensitive to illumination change and shows better performance in characterizing object shape and appearance. Each of the boosting HOG feature is an output of an adaboost classifier, which is trained using all bins upon a cell in traditional HOG features. All boosting HOG features are combined to establish the final feature vector to train a linear SVM classifier for vehicle classification. Compared with classical approaches, the proposed method achieved better performance in higher detection rate, lower false positive rate and faster detection speed.

Metadata

Item Type:	Book Section
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	07 Jun 2013 10:51
Last Modified:	09 Aug 2023 12:33
URI:	https://eprints.bbk.ac.uk/id/eprint/7381

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