An optimized hierarchical classifier for pedestrian detection
Xu, Yanwu; Cao, Xianbin; Qiao, Hong
Conference Name7th World Congress on Intelligent Control and Automation
Conference DateJUN 25-27, 2008
Conference PlaceChongqing, PEOPLES R CHINA
AbstractClassification 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.
KeywordPedestrian Detection Hierarchical Classifier Adaboost
Document Type会议论文
Corresponding AuthorXu, Yanwu
AffiliationUniv Sci & Technol China, Dept Comp Sci & Technol
Recommended Citation
GB/T 7714
Xu, Yanwu,Cao, Xianbin,Qiao, Hong. An optimized hierarchical classifier for pedestrian detection[C],2008.
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