An optimized hierarchical classifier for pedestrian detection

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	An optimized hierarchical classifier for pedestrian detection
	Xu, Yanwu; Cao, Xianbin; Qiao, Hong
	2008
会议名称	7th World Congress on Intelligent Control and Automation
会议录名称	2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION
会议日期	JUN 25-27, 2008
会议地点	Chongqing, PEOPLES R CHINA
摘要	Classification is an essential technology in Pedestrian Detection System (PDS). Until now, single-classifier and basic cascaded classifier had been widely used in PDS; however, most of them can hardly satisfy the 3 requirements at the same time: high detection speed, high detection rate and low false positive rate. In this paper, we proposed an optimized hierarchical classifier which can satisfy the 3 requirements. The proposed method adopted Corse-to-fine and Early-rejection principles to achieve global high performance. It consists of two hierarchies, the first one is used to quickly reject non-pedestrian objects and select out only a few candidates; the second one makes further verification to these candidates. Furthermore, each hierarchy was optimized with statistical models basing on experiments; and each hierarchy is a treelike classifier which has specific optimization demands. At last; an overall performance evaluation standard is proposed, and the experimental results showed that the proposed classifier had better overall performance.
关键词	Pedestrian Detection Hierarchical Classifier Adaboost
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12814
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
通讯作者	Xu, Yanwu
作者单位	Univ Sci & Technol China, Dept Comp Sci & Technol
推荐引用方式 GB/T 7714	Xu, Yanwu,Cao, Xianbin,Qiao, Hong. An optimized hierarchical classifier for pedestrian detection[C],2008.