Figure 1. Pedestrian images in real environment Multi Features Combination for Pedestrian Detection

Abstract—In this paper, we propose a new approach for pedestrian detection in crowded scene from static images. The method is based on hybrid features, one type of middle-level 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. We 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. Adaboost is used in each window to get one middle-level feature which composes of some best features including gradient features, Edgelet features and haar-like features in this window. Secondly, from all middle-level features, Adaboost is used again to get the final classifier. Experiment results on common datasets and comparisons with some previous methods are given. Index Terms—pedestrian detection, multi features, gradien