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dc.contributor.advisorAude Oliva and Mathew Monfort.en_US
dc.contributor.authorZhou, Diane Yue.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2021-01-06T18:31:16Z
dc.date.available2021-01-06T18:31:16Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/129144
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020en_US
dc.descriptionCataloged from student-submitted PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages ).en_US
dc.description.abstractGaze 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.en_US
dc.description.statementofresponsibilityby Diane Yue Zhou.en_US
dc.format.extentpagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleGaze Prediction in First-Person View Videosen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1227276695en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2021-01-06T18:31:15Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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