Automatic pedestrian detection in partially occluded single image

Abstract

In this study, we propose a novel method to process crowds and partially occluded pedestrians during single-image pedestrian detection. First, two procedures are proposed and developed to extract features at different body parts of pedestrian from images. One procedure uses the multiscale block-based histogram of oriented gradients, which is preprocessed via Gabor filtering, to effectively enhance the descriptions of features of pedestrians’ heads and bodies. The other involves modifying the Haar-like features as parallelogram-based Haar-like features to suitably depict pedestrians’ legs and arms, which are typically not straight while walking. In addition, the computations of features are expedited through integral image acceleration for these two extraction methods. After the pedestrian features are acquired, a two-tier support vector machine (SVM) classifier is proposed for processing partially occluded pedestrians. The first-tier SVM classifier is used to judge if there are any occluded body parts. Next, classification probabilities of nonoccluded body parts in first-tier one are input into the second-tier classifier to determine if there are any pedestrians in the detection window. Compared with six state-of-the-art approaches, the experimental results indicate that the proposed method is more accurate and satisfactory in terms of the receiver operating characteristic curve and four other criteria. Additionally, our method effectively processes images in which a crowd is present or pedestrians are partially occluded and enables pedestrian detection in images of different scenes.