Part-based pedestrian detection using HoG features and vertical symmetry

This paper describes a new approach for pedestrian detection in traffic scenes. The originality of the method resides in the combination of the benefits of the symmetry characteristic for pedestrians in intensity images and the benefits of deformable part-based models for recognizing pedestrians in multiple object hypotheses generated by a stereo vision system. A mixture model based on several pedestrian attitudes is used for addressing the large intraclass variability that pedestrians may have (they may have different poses and attitudes like: standing, walking, running etc). We have used a probabilistic approach based on support vector machine (SVM) and histograms of gradient orientations (HoG) features for pedestrian classification.