Pedestrian Detection Based on a New Two-Step Framework

In this paper, we propose a new framework in pedestrian detection using a two-step classification algorithm, which is a ¿coarse to fine¿ course. The framework consists of a full-body detection (FBD) step and a head-shoulder detection (HSD) step. The FBD step uses fusion of Haar-like and HOG features to get better performance, and the HSD step utilizes edgelet features for classification and detection. The pedestrian data is obtained from MIT, INRIA dataset and surveillance videos for training. The experiment carried out on videos from campus and CAVIAR dataset illustrates that the proposed method is robust and feasible enough for pedestrian detection and could handle occlusions more accurately than other methods.