Gaze Prediction in First-Person View Videos
Name
1227276695-MIT.pdf
Size
37.43 MB
Format
Adobe PDF
Checksum (MD5)
a880744236194cae40be4eb156e32f26
Author(s)
Zhou, Diane Yue.
Advisor(s)
Aude Oliva and Mathew Monfort.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Gaze is an important topic in computer vision as it reveals points of interest that tend to capture a subject's attention in a scene and potential intentions of the subject of gaze. Gaze data is becoming more readily obtainable with technological advances in wearable cameras, enabling the potential for more accurate first-person view gaze prediction models and interesting analyses of gaze. In this research, we use gaze data collected from Pupil Labs glasses to build and compare several gaze prediction models. Our models predict the location of gaze in each frame of a first-person view video by leveraging convolutional neural networks based solely on visual saliency maps. We believe that future work in incorporating more context information about the camera wearer's behavior and the scenes in the videos would further improve the model's performance.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages ).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
Persistent DSpace Link
