Head Pose Estimation for an Omnidirectional Camera Using a Convolutional Neural Network

Abstract

The human head pose provides insights on the activities or intentions of a given person. Head pose estimation techniques are thus often employed in intelligent surveillance camera systems for marketing analysis or security monitoring. Nowadays, omnidirectional cameras have become widely used in surveillance systems owing to their unique property of wide-range coverage. However, this property causes significant changes in visual appearance and distortions inside the image, and general approaches using a head image may fail in estimation. In this paper, we thus propose a method for head pose estimation using omnidirectional camera images. The proposed model employs both a head image and full body image for cases in which a face is self-occluded and the head image is thus almost useless. In addition, image attribute data are integrated into the network to learn the relation between the changes in appearance or distortion and locations inside the whole image. Experiments are conducted to compare the accuracy of the presented approach with those of ordinary methods. It is verified that the proposed method improves the accuracy by more than 19% over the baseline method.