Official websites use. Share sensitive information only on official, secure websites. Point-of-gaze estimation is part of a larger set of tasks aimed at improving user experience, providing business insights, or facilitating interactions with different devices. There has been a growing interest in this task, particularly due to the need for upgrades in e-meeting platforms during the pandemic when on-site activities were no longer possible for educational institutions, corporations, and other organizations.
Current research advancements are focusing on more complex methodologies for data collection and task implementation, creating a gap that we intend to address with our contributions. Thus, we introduce a methodology for data acquisition that shows promise due to its nonrestrictive and straightforward nature, notably increasing the yield of collected data without compromising diversity or quality.
Additionally, we present a novel and efficient convolutional neural network specifically tailored for calibration-free point-of-gaze estimation that outperforms current state-of-the-art methods on the MPIIFaceGaze dataset by a substantial margin, and sets a strong baseline on our own data. Keywords: computer vision, convolutional neural networks, point of gaze, deep learning.
In recent years, the proliferation of e-meeting platforms has underscored the critical need to enhance user experience in these settings.
Visual attention analysis has surged in popularity, demanding the development of more robust real-time machine learning models and the acquisition of gaze-point estimation data from basic consumer-grade RGB sensors. While similar applications like gaze-based human—computer interaction and user-state awareness have been extensively explored, point-of-gaze PoG estimation remains a persistent and crucial issue in both academic and industrial domains.
