Fast and robust ellipse detection algorithm for head-mounted eye tracking systems

Abstract

In head-mounted eye tracking systems, the correct detection of pupil position is a key factor in estimating gaze direction. However, this is a challenging issue when the videos are recorded in real-world conditions, due to the many sources of noise and artifacts that exist in these scenarios, such as rapid changes in illumination, reflections, occlusions and an elliptical appearance of the pupil. Thus, it is an indispensable prerequisite that a pupil detection algorithm is robust in these challenging conditions. In this work, we present one pupil center detection method based on searching the maximum contribution point to the radial symmetry of the image. Additionally, two different center refinement steps were incorporated with the aim of adapting the algorithm to images with highly elliptical pupil appearances. The performance of the proposed algorithm is evaluated using a dataset consisting of 225,569 head-mounted annotated eye images from publicly available sources. The results are compared with the better algorithm found in the bibliography, with our algorithm being shown as superior.

1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning

“…Another improvement in pupil shape reconstruction was the evaluation of individual segments [52]. Alternative to edge detection, the radial symmetry transform was used to detect the pupil center [60].…”

Gaze Estimation Based on Convolutional Structure and Sliding Window-Based Attention Mechanism

“…The model-based gaze estimation method typically involves using eye information, such as iris radius, kappa angle, and pupil position, to create a geometric model for prediction [14,15]. These methods frequently require specialized equipment to capture specific eye information [16,17], which can be costly and have limited applications. As shown in Figure 1a, the appearance-based gaze estimation method does not require specialized equipment and directly learns mapping functions from images in the gaze direction, but requires enormous training data.…”