Pedestrian orientation estimation

Abstract

This paper addresses the task of estimating the orientation of pedestrians from monocular images provided by an automotive camera. From an initial detection of a pedestrian, we analyze the area within their bounding box and give an estimation of the orientation. Using ground truth mocap data, we define the orientations as a direction and a rough human pose. A random forest classifier trained on this data using HOG features assigns each detected pedestrian to their orientation cluster. Evaluation of the method is performed on a new dataset and on a publicly available dataset showing improved results.