Multi Features Combination for Pedestrian Detection

In this paper, the authors propose a new approach for pedestrian detection in crowded scene from static images. The method is based on hybrid features, one type of middlelevel features, which compose of multi features include gradient features, Edgelet features and haar-like features, three low-level feature sets. The gradient features focus on the local point information, the Edgelet features focus on the local edge information and the haar-like features focus on the local region information of the image. They use two stages of Adaboost to train the final classifier. In the first stage, the whole image is divided into many small windows which all include numerous low-level features.