Head detection and orientation estimation for pedestrian safety

Head detection and orientation estimation are a vital component in the intention recognition of pedestrians. In this paper we propose a novel framework to detect highly occluded pedestrians and estimate their head orientation. Detection is performed for pedestrian´s heads only. For this we employ a part-based classifier with HOG/SVM combinations. Head orientations are estimated using discrete orientation classifiers and LBP features. Results are improved by leveraging orientation estimation for head and torso as well as motion information. The orientation estimation is integrated over time using a Hidden Markov Model. From the discrete model we obtain a contiunous head orientation. We evaluate our approach on image sequences with ground truth orientation measurements. To our best knowledge we outperform state of the art results.