Single-User 2D Gaze Estimation in Retail Environment Using Deep Learning

Abstract

Human gaze estimation is a widely used technique to observe human behavior. The rapid adaptation of deep learning techniques in gaze estimation has evolved human gaze estimation to many application domains. The retail industry is one domain with challenging unconstrained environmental conditions such as eye occlusion and personal calibration. This study presents a novel gaze estimation model for single-user 2D gaze estimation in a retail environment. Our novel architecture, inspired by the previous work in gaze following, models the scene and head feature and further utilizes a shifted grids technique to accurately predict a saliency map. Our results show that the model can effectively infer 2D gaze in a retail environment. We achieve state-of-the-art performance on Gaze On Objects (GOO) dataset. The obtained results have shown 25.2° angular error for gaze estimation. Furthermore, we provide a detailed analysis of the GOO dataset and comprehensively analyze the selected model feature extractor to support our results.