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

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	Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos
	CaoXianbin; WuChangxia; YanPingkun; LiXuelong; Cao Xianbin
	2011
会议名称	2011 18th IEEE International Conference on Image Processing, ICIP 2011
会议录名称	Proceedings - International Conference on Image Processing, ICIP
页码	2421-2424
会议日期	September 11, 2011 - September 14, 2015
会议地点	Brussels, Belgium
出版地	445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States
出版者	IEEE Computer Society
会议主办者	IEEE; IEEE Signal Processing Society
产权排序	3
摘要	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.
关键词	Vehicle Detection Boosting Hog Feature Linear Svm Urban Environment
作者部门	光学影像分析与学习中心
收录类别	EI
语种	英语
ISSN号	1522-4880
文献类型	会议论文
条目标识符	http://ir.opt.ac.cn/handle/181661/20138
专题	瞬态光学研究室
通讯作者	Cao Xianbin
推荐引用方式 GB/T 7714	CaoXianbin,WuChangxia,YanPingkun,et al. Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos[C]. 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States:IEEE Computer Society,2011:2421-2424.

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