Classification is an essential technology inPedestrian Detection System (PDS). Until now, single-classifierand basic cascaded classifier had been widely used in PDS;however, most of them can hardly satisfy the 3 requirements atthe same time: high detection speed, high detection rate and lowfalse positive rate. In this paper, we proposed an optimizedhierarchical classifier which can satisfy the 3 requirements. Theproposed method adopted Corse-to-fine and Early-rejectionprinciples to achieve global high performance. It consists of twohierarchies, the first one is used to quickly reject non-pedestrianobjects and select out only a few candidates; the second onemakes further verification to these candidates. Furthermore,each hierarchy was optimized with statistical models basing onexperiments; and each hierarchy is a treelike classifier which hasspecific optimization demands. At last, an overall performanceevaluation standard is proposed, and the experimental resultsshowed that the proposed classifier had better overallperformance.