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Exploiting multispectral and contextual information to improve human detection

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TitleInfo
Title
Exploiting multispectral and contextual information to improve human detection
Name (type = personal)
NamePart (type = family)
Liu
NamePart (type = given)
Jingjing
NamePart (type = date)
1985-
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Jingjing Liu
Role
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author
Name (type = personal)
NamePart (type = family)
Metaxas
NamePart (type = given)
Dimitris N.
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Dimitris N. Metaxas
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Advisory Committee
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chair
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Bekris
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Kostas
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Kostas Bekris
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Yu
NamePart (type = given)
Jingjin
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Jingjin Yu
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Ratha
NamePart (type = given)
Nalini K.
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Nalini K. Ratha
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
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Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
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Text
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theses
OriginInfo
DateCreated (qualifier = exact)
2017
DateOther (type = degree); (qualifier = exact)
2017-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2017
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Human detection has various applications, e.g., autonomous driving car, surveillance system, and retail. In this dissertation, we first exploit multispectral images (i.e., RGB and thermal images) for human detection. We extensively analyze Faster R-CNN for the detection task and then model multispectral human detection into a fusion problem of convolutional networks (ConvNets). We design four distinct ConvNet fusion architectures that integrate two-branch ConvNets on different stages of neural networks, all of which yield better performance compared with the baseline detector. In the second part of this dissertation, we leverage instance-level contextual information in crowded scenes to boost performance of human detection. Based on a context graph that incorporates both geometric and social contextual patterns from crowds, we apply progressive potential propagation algorithm to discover weak detections that are contextually compatible with true detections while suppressing irrelevant false alarms. The method significantly improves the performance of any shallow human detectors, obtaining comparable results to deep learning based methods.
Subject (authority = RUETD)
Topic
Computer Science
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_8328
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xiv, 81 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Robotics--Human factors
Subject (authority = ETD-LCSH)
Topic
Human-robot interaction
Note (type = statement of responsibility)
by Jingjing Liu
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3MC935C
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Liu
GivenName
Jingjing
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-09-06 23:29:46
AssociatedEntity
Name
Jingjing Liu
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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

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2017-09-06T23:10:35
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2017-09-06T23:10:35
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