Publication Date

Fall 2021

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Nada Attar

Second Advisor

Mark Stamp

Third Advisor

Noha Elfiky

Keywords

Visual Search, Visual Attention, Eye Tracking, Machine Learning, Random Forests, Deep Learning, Convolutional Neural Networks, Computer Vision

Abstract

In an average human life, the eyes not only passively scan visual scenes, but most times end up actively performing tasks including, but not limited to, searching, comparing, and counting. As a result of the advances in technology, we are observing a boost in the average screen time. Humans are now looking at an increasing number of screens and in turn images and videos. Understanding what scene a user is looking at and what type of visual task is being performed can be useful in developing intelligent user interfaces, and in virtual reality and augmented reality devices. In this research, we run machine learning and deep learning algorithms to identify the task type from eye-tracking data. In addition to looking at raw numerical data, we take a “visual” approach by experimenting on variations of Computer Vision algorithms like Convolutional Neural Networks on the visual representations of the user gaze scan paths. We compare the results of our visual approach to the classic algorithm of random forests.

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