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

• Published in: 2011 18th IEEE International Conference on Image Processing pp. 2421 - 2424
• Main Authors: Xianbin Cao, Changxia Wu, Pingkun Yan, Xuelong Li
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2011
• Subjects: Boosting; boosting HOG feature; Feature extraction; linear SVM; Support vector machines; Training; urban environment; Vehicle detection; Vehicles; Videos
• ISBN: 1457713047; 9781457713040
• ISSN: 1522-4880
• DOI: 10.1109/ICIP.2011.6116132

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.