3D Gaze Tracking for Studying Collaborative Interactions in Mixed-Reality Environments

TL;DR

This paper introduces a new 3D gaze tracking framework for mixed-reality environments that improves accuracy and real-time tracking of multiple users without specialized hardware, advancing collaborative interaction analysis.

Contribution

The novel framework combines computer vision and machine learning to enable precise, hardware-independent 3D gaze estimation in group mixed-reality settings.

Findings

Achieves real-time, multi-user gaze tracking with high accuracy.

Addresses depth estimation errors and maintains spatial and identity consistency.

Demonstrates effectiveness in educational and professional training scenarios.

Abstract

This study presents a novel framework for 3D gaze tracking tailored for mixed-reality settings, aimed at enhancing joint attention and collaborative efforts in team-based scenarios. Conventional gaze tracking, often limited by monocular cameras and traditional eye-tracking apparatus, struggles with simultaneous data synchronization and analysis from multiple participants in group contexts. Our proposed framework leverages state-of-the-art computer vision and machine learning techniques to overcome these obstacles, enabling precise 3D gaze estimation without dependence on specialized hardware or complex data fusion. Utilizing facial recognition and deep learning, the framework achieves real-time, tracking of gaze patterns across several individuals, addressing common depth estimation errors, and ensuring spatial and identity consistency within the dataset. Empirical results demonstrate the accuracy and reliability of our method in group environments. This provides mechanisms for significant advances in behavior and interaction analysis in educational and professional training applications in dynamic and unstructured environments.