[Paper] Estimation of the Gaze Direction for First-person Perspective Images Based on Surveillance Cameras

Abstract

The proliferation of surveillance cameras and advancements in image analysis technology have facilitated the extraction of extensive information from images. However, these techniques extract features from the person's region in an image and provide information based on the person's appearance. This paper proposes a method for estimating the areas of interest of a person captured in an image by reconstructing the image from the first-person perspective. In this study, we aim to reconstruct a subject-viewpoint image by estimating the gaze direction of the person captured in an image. To achieve this, we propose a method that utilizes keypoints from 3D posture estimation for gaze direction estimation. Compared with a deep-neural-network-based approach that directly estimates the gaze direction from images, the proposed method exhibits comparable accuracy and processing speeds. In addition, our subject experiment reveals the characteristics of our method and the challenges.