A deeper look at hand pose estimation
Name
1098178842-MIT.pdf
Size
17.34 MB
Format
Adobe PDF
Checksum (MD5)
9195f945958af345e780e066a2197451
Author(s)
Myanganbayar, Battushig.
Advisor(s)
Boris Katz.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Hand pose recognition is a fundamental human ability and an important, yet elusive, goal for computer vision research. One of the major challenges in hand pose recognition is the sheer scale of the problem. The human hand is a notoriously agile object with 27 degrees of freedom. In a sense, it is an impossible task to collect a dataset with every major hand pose configuration. However, current state-of-the-art approaches rely too much on training data and generalize poorly to unseen hand poses. Furthermore, current benchmarking datasets are of poor quality and contain test sets that are highly correlated with the training set, which in turn encourages the development of data-reliant techniques for better accuracy only on paper. In this thesis, I introduce a better and more realistic benchmarking dataset, and propose a novel approach for hand pose detection that has the potential to generalize better to unseen hand poses.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 77-79).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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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.
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