Head Orientation Estimation using Gait Observation

Abstract

We propose a novel method to estimate the head orientation of a pedestrian. There have been many methods for head orientation estimation based on facial textures of pedestrians. It is, however, impossible to apply these methods to low-resolution images which are captured by a surveillance camera at a distance. To deal with the problem, we construct a method that is not based on facial textures but on gait features, which are robustly obtained even from low-resolution images. In our method, first, size-normalized silhouette images of pedestrians are generated from captured images. We then obtain the Gait Energy Image (GEI) from the silhouette images as a gait feature. Finally, we generate a discriminant model to classify their head orientation. For this training step, we build a dataset consisting of gait images of over 100 pedestrians and their head orientations. In evaluation experiments using the dataset, we classified their head orientation by the proposed method. We confirmed that gait changes of the whole body were efficient for the estimation in quite low-resolution images which existing methods cannot deal with due to the lack of facial textures.