Design and Implementation of an Eye-Tracking System with OpenCV and Synthetic Data

Abstract

Eye-tracking technology is instrumental in assistive devices, especially for individuals with limited motor abilities. This paper presents an eye-tracking system developed using OpenCV, with a focus on pupil detection and gaze direction estimation. By leveraging synthetic data alongside appearance-based models, this system enhances accuracy and adaptability, addressing common challenges in dynamic environments. The approach seeks for improve robustness of eye-tracking systems in this world applications, particularly in assistive technologies. Eye-tracking technology is crucial to the in assistive devices, particularly for individuals with motor impairments. These systems facilitate user interaction through gaze control, greatly improving life quality. This paper presents an eye-tracking system developed using OpenCV, focusing on detecting pupil position and estimating gaze direction with image processing techniques.  The approach incorporates synthetic data and appearance-based models to enhance system adaptability and accuracy in real-world scenarios.  By addressing limitations in traditional systems, including environmental sensitivity, this method provides a robust solution for dynamic environments.  The study identifies potential applications in assistive technology, promoting more adaptable and accurate control systems.