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Modeling human motion using manifold learning and factorized generative models

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TitleInfo
Title
Modeling human motion using manifold learning and factorized generative models
Name (type = personal)
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Lee
NamePart (type = given)
Chan-Su
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Chan-Su Lee
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author
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Ahmed
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Advisory Committee
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Ahmed Elgammal
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chair
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Metaxas
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Dimitris
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Advisory Committee
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Dimitris N Metaxas
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internal member
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Pavlovic
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Vladimir
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Advisory Committee
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Vladimir Pavlovic
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Neumann
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Jan
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Advisory Committee
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Jan Neumann
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Rutgers University
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degree grantor
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Graduate School-New Brunswick
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Text
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theses
OriginInfo
DateCreated (qualifier = exact)
2007
DateOther (qualifier = exact); (type = degree)
2007
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English
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electronic
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application/pdf
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xxv, 194 pages
Abstract
Modeling the dynamic shape and appearance of articulated moving objects is essential for human motion analysis, tracking, synthesis, and other computer vision problems. Modeling the shape and appearance of human motion is challenging due to the high dimensionality of the articulated human motion, variations of shape and appearance from different views and in different people, and the nonlinearity in shape and appearance deformations in the observed sequences. Recent interest in modeling human motion is originated from the various potential real-world applications such as visual surveillance, human-computer interaction, video analysis, computer animation, etc.
We present a novel framework to model dynamic shape and appearance using nonlinear manifold embedding and factorization. We investigate different representations to embed high-dimensional human motion sequences in low dimensional spaces by supervised and unsupervised manifold learning techniques to achieve representations that capture the intrinsic structure of the motion. Nonlinear dimensionality reduction techniques based on visual data and kinematic data are applied to discover low dimensional intrinsic manifold representation for body configuration. Also, we investigate the use of supervised manifold learning from a known manifold topology to model deformation of manifolds from an ideal case. By learning nonlinear mapping from the embedding space to the input shape or appearance, we can generate shape and appearance sequences according to the motion state on the embedded manifold.
We present a decomposable generative model to analyze shape and appearance variations by different factors such as person's style, motion type, and view point. We use multilinear analysis in the nonlinear mapping coefficient space to factorize shape and appearance variations. Also, we investigate learning generative models to represent continuous body configuration and continuous view manifolds in a product space (i.e. body configuration manifold x view manifold). The proposed factorized generative models provide rich models for the analysis of dynamic shape and appearance of human motion. We applied the model in computer vision problems such as inferring 3D body pose from 2D images, tracking human motion with continuous view variations within the Bayesian framework, and gait recognition. We also applied our model for facial expression analysis, tracking, recognition and synthesis.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references (p. 183-192).
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Image processing
Subject (authority = ETD-LCSH)
Topic
Pattern recognition systems
Subject (authority = ETD-LCSH)
Topic
Topological manifolds
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.13479
Identifier
ETD_241
Identifier (type = doi)
doi:10.7282/T39S1RGK
Location
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NjNbRU
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
Copyright
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Copyright protected
Availability
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Open
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Name
Chan-Su Lee
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Copyright holder
Affiliation
Rutgers University. Graduate School-New Brunswick
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Non-exclusive ETD license
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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