MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Large scale video action understanding

Author(s)
Yan, Tom, M. Eng. Massachusetts Institute of Technology
Thumbnail
DownloadFull printable version (3.671Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Aude Oliva.
Terms of use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
Metadata
Show full item record
Abstract
The goal of the project is to build a large scale video dataset called Moments, and train existing/novel models for action recognition. To aid automation of video collection and annotation selection, I trained Convolutional Neural Network models to estimate the likelihood of a desired action appearing in video clips. Selecting clips, which are highly probable to contain the wanted action, for annotation leads to a more efficient process overall with higher yield. Once a sizable dataset had been amassed, I investigated new multi-modal models that make use of different (spatial, temporal, auditory) signals in the video. I also conducted preliminary experiments into several promising directions that Moments opens up, including multi-label training. Lastly, I trained baseline models on Moments to calibrate the performance of existing techniques. Post-training, I diagnosed the shortcomings of the models and visualized videos that were found to be particularly difficult. I discovered that the difficulty largely arises due to the great variety in quality/perspective/subjects found in Moments videos. This highlights the challenging nature of the dataset and its value to the research community.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
 
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
 
Cataloged from student-submitted PDF version of thesis.
 
Includes bibliographical references (pages 37-39).
 
Date issued
2017
URI
http://hdl.handle.net/1721.1/119541
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.