accelerating vehicle detection in low-altitude airborne urban video

ISCAS OpenIR

	accelerating vehicle detection in low-altitude airborne urban video
	Cao Xianbin; Lin Renjun; Yan Pingkun; Li Xuelong
	2011
会议名称	6th International Conference on Image and Graphics, ICIG 2011
会议录名称	Proceedings - 6th International Conference on Image and Graphics, ICIG 2011
页码	648-653
会议日期	August 12, 2011 - August 15, 2011
会议地点	Hefei, Anhui, China
收录类别	EI
ISBN	9780769545417
部门归属	(1) Anhui Province Key Laboratory of Software In Computing and Communication School of Computer Science and Technology University of Science and Technology of China Hefei 230027 Anhui China; (2) State Key Laboratory of Transient Optics and Photonics Center for OPTical IMagery Analysis and Learning (OPTIMAL) Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an 710119 Shaanxi China
摘要	The limitation of the existing methods of traffic data collection is that they rely on techniques that are strictly local in nature. The airborne system in unmanned aircrafts provides the advantages of wider view angle and higher mobility. However, detecting vehicles in airborne videos is a challenging task because of the scene complexity and platform movement. Most of the techniques used in stationary platforms cannot perform well in this situation. A new and efficient method based on Bayes model is proposed in this paper. This method can be divided into two stages, attention focus extraction and vehicle classification. Experimental results demonstrated that, compared with other representative algorithms, our method obtained better performance with higher detection rate, lower false positive rate and faster detection speed. © 2011 IEEE.; The limitation of the existing methods of traffic data collection is that they rely on techniques that are strictly local in nature. The airborne system in unmanned aircrafts provides the advantages of wider view angle and higher mobility. However, detecting vehicles in airborne videos is a challenging task because of the scene complexity and platform movement. Most of the techniques used in stationary platforms cannot perform well in this situation. A new and efficient method based on Bayes model is proposed in this paper. This method can be divided into two stages, attention focus extraction and vehicle classification. Experimental results demonstrated that, compared with other representative algorithms, our method obtained better performance with higher detection rate, lower false positive rate and faster detection speed. © 2011 IEEE.
关键词	Adaptive Boosting
主办者	National Natural Science Foundation of China; Chinese Academy of Science; Microsoft Research Asia; Xian Institute of Optics and Precision Mechanics of CAS; Anhui Crearo Technology Co., Ltd
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16319
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Cao Xianbin,Lin Renjun,Yan Pingkun,et al. accelerating vehicle detection in low-altitude airborne urban video[C],2011:648-653.