EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking

Autor:	Kothari, Rakshit S., Chaudhary, Aayush K., Bailey, Reynold J., Pelz, Jeff B., Diaz, Gabriel J.
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1109/TVCG.2021.3067765
Popis:	Ellipse fitting, an essential component in pupil or iris tracking based video oculography, is performed on previously segmented eye parts generated using various computer vision techniques. Several factors, such as occlusions due to eyelid shape, camera position or eyelashes, frequently break ellipse fitting algorithms that rely on well-defined pupil or iris edge segments. In this work, we propose training a convolutional neural network to directly segment entire elliptical structures and demonstrate that such a framework is robust to occlusions and offers superior pupil and iris tracking performance (at least 10$\%$ and 24$\%$ increase in pupil and iris center detection rate respectively within a two-pixel error margin) compared to using standard eye parts segmentation for multiple publicly available synthetic segmentation datasets. Comment: Code available at https://bitbucket.org/RSKothari/ellseg/src/master/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.09600 Zobrazit plný text záznamu View this record from Arxiv