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Towards accurate group activity analysis in videos

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TitleInfo
Title
Towards accurate group activity analysis in videos
SubTitle
robust saliency detection and effective feature modeling
Name (type = personal)
NamePart (type = family)
Cui
NamePart (type = given)
Xinyi
NamePart (type = date)
1983-
DisplayForm
Xinyi Cui
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Metaxas
NamePart (type = given)
Dimitris N
DisplayForm
Dimitris N Metaxas
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Pavlovic
NamePart (type = given)
Vladimir
DisplayForm
Vladimir Pavlovic
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Eliassi-Rad
NamePart (type = given)
Tina
DisplayForm
Tina Eliassi-Rad
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Huang
NamePart (type = given)
Xiaolei
DisplayForm
Xiaolei Huang
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2013
DateOther (qualifier = exact); (type = degree)
2013-05
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Human activity analysis is an important area of computer vision research today. The goal of human activity analysis is to automatically analyze ongoing activities from an unknown video. The ability to analyze complex human activities from videos has many important applications, such as smart camera system, video surveillance, etc. However, it is still far from an off-the-shelf system. There are many challenging problems and it is still an active research area. This dissertation focuses on addressing two problems: various camera motions and effective modeling of group behaviors. We propose a unified and robust framework to detect salient motions from diverse types of videos. Given a video sequence that is recorded from either a stationary or moving camera, our algorithm is able to detect the salient motion regions. The model is inspired by two observations: 1) background motion caused by orthographic cameras lies in a low rank subspace, and 2) pixels belonging to one trajectory tend to group together. Based on these two observations, we introduce a new model using both low rank and group sparsity constraints. It is able to robustly decompose a motion trajectory matrix into foreground and background ones. Extensive experiments demonstrate very competitive performance on both synthetic data and real videos. After salient motion detection, a new method is proposed to model group behaviors in video sequences. This approach effectively models group activities based on social behavior analysis. Different from previous work that uses independent local features, our method explores the relationships between the current behavior state of a subject and its actions. An interaction energy potential function is proposed to represent the current behavior state of a subject, and velocity is used as its actions. Our method does not depend on human detection, so it is robust to detection errors. Instead, tracked salient points are able to provide a good estimation of modeling group interaction. We evaluate our algorithm in two datasets: UMN and BEHAVE. Experimental results show its promising performance against the state-of-art methods.
Subject (authority = RUETD)
Topic
Computer Science
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_4617
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xiii, 71 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Xinyi Cui
Subject (authority = ETD-LCSH)
Topic
Computer vision--Research
Subject (authority = ETD-LCSH)
Topic
Human activity recognition--Research
Subject (authority = ETD-LCSH)
Topic
Video surveillance
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000068833
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T37P8X0C
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Cui
GivenName
Xinyi
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-04-11 20:59:40
AssociatedEntity
Name
Xinyi Cui
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
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