iCatcher: A neural network approach for automated coding of young children's eye movements.
Infancy
; 27(4): 765-779, 2022 07.
Article
em En
| MEDLINE
| ID: mdl-35416378
ABSTRACT
Infants' looking behaviors are often used for measuring attention, real-time processing, and learning-often using low-resolution videos. Despite the ubiquity of gaze-related methods in developmental science, current analysis techniques usually involve laborious post hoc coding, imprecise real-time coding, or expensive eye trackers that may increase data loss and require a calibration phase. As an alternative, we propose using computer vision methods to perform automatic gaze estimation from low-resolution videos. At the core of our approach is a neural network that classifies gaze directions in real time. We compared our method, called iCatcher, to manually annotated videos from a prior study in which infants looked at one of two pictures on a screen. We demonstrated that the accuracy of iCatcher approximates that of human annotators and that it replicates the prior study's results. Our method is publicly available as an open-source repository at https//github.com/yoterel/iCatcher.
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1
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Movimentos Oculares
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article