ISCAS OpenIR
accelerating vehicle detection in low-altitude airborne urban video
Cao Xianbin; Lin Renjun; Yan Pingkun; Li Xuelong
2011
Conference Name6th International Conference on Image and Graphics, ICIG 2011
SourceProceedings - 6th International Conference on Image and Graphics, ICIG 2011
Pages648-653
Conference DateAugust 12, 2011 - August 15, 2011
Conference PlaceHefei, Anhui, China
Indexed TypeEI
ISBN9780769545417
Department(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
English AbstractThe 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.
KeywordAdaptive Boosting
SponsorshipNational 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
Language英语
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/16319
Collection中国科学院软件研究所
Recommended Citation
GB/T 7714
Cao Xianbin,Lin Renjun,Yan Pingkun,et al. accelerating vehicle detection in low-altitude airborne urban video[C],2011:648-653.
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