An Optimized Hierarchical Classifier for Pedestrian Detection

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.