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Human action attribute learning from video data using low-rank representations

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Text
TitleInfo
Title
Human action attribute learning from video data using low-rank representations
Name (type = personal); (authority = orcid); (authorityURI = http://id.loc.gov/vocabulary/identifiers/orcid.html); (valueURI = http://orcid.org/0000-0002-8935-1629)
NamePart (type = family)
Wu
NamePart (type = given)
Tong
Affiliation
Electrical and Computer Engineering, Rutgers University
Role
RoleTerm (type = text); (authority = marcrt)
author
Name (type = personal)
NamePart (type = family)
Gurram
NamePart (type = given)
Prudhvi
Affiliation
Army Research Lab
Role
RoleTerm (type = text); (authority = marcrt)
author
Name (type = personal)
NamePart (type = family)
Rao
NamePart (type = given)
Raghuveer M.
Affiliation
Army Research Lab
Role
RoleTerm (type = text); (authority = marcrt)
author
Name (type = personal); (authority = orcid); (authorityURI = http://id.loc.gov/vocabulary/identifiers/orcid.html); (valueURI = http://orcid.org//0000-0003-4406-526)
NamePart (type = family)
Bajwa
NamePart (type = given)
Waheed U.
Affiliation
Electrical and Computer Engineering, Rutgers University
Role
RoleTerm (type = text); (authority = marcrt)
author
Name (type = corporate); (authority = RutgersOrg-Department)
NamePart
Electrical and Computer Engineering
Name (type = corporate); (authority = RutgersOrg-School)
NamePart
School of Engineering
Genre (authority = RULIB-FS)
Other
Genre (authority = NISO JAV)
Unidentified version of a published work
Genre (authority = marcgt)
technical report
Genre (authority = ExL-Esploro)
Technical documentation
Note
Technical Report #2020-07-001
OriginInfo
DateCreated (encoding = w3cdtf); (qualifier = exact); (keyDate = yes)
2020
Abstract (type = Abstract)
Representation of human actions as a sequence of human body movements or action attributes enables the development of models for human activity recognition and summarization. We present an extension of the low-rank representation (LRR) model, termed the clustering-aware structure-constrained low-rank representation (CS-LRR) model, for unsupervised learning of human action attributes from video data. Our model is based on the union-of-subspaces (UoS) framework, and integrates spectral clustering into the LRR optimization problem for better subspace clustering results. We lay out an efficient linear alternating direction method to solve the CS-LRR optimization problem. We also introduce a hierarchical subspace clustering approach, termed hierarchical CS-LRR, to learn the attributes without the need for a priori specification of their number. By visualizing and labeling these action attributes, the hierarchical model can be used to semantically summarize long video sequences of human actions at multiple resolutions. A human action or activity can also be uniquely represented as a sequence of transitions from one action attribute to another, which can then be used for human action recognition. We demonstrate the effectiveness of the proposed model for semantic summarization and action recognition through comprehensive experiments on five real-world human action datasets.
PhysicalDescription
InternetMediaType
application/pdf
Extent
1 online resource (26 pages) : illustrations
Subject (authority = local)
Topic
Action recognition
Subject (authority = local)
Topic
Semantic summarization
Subject (authority = local)
Topic
Subspace clustering
Subject (authority = local)
Topic
Union of subspaces
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
RelatedItem (type = host)
TitleInfo
Title
Bajwa, Waheed U.
Identifier (type = local)
rucore30246100001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-t7fe-4a02
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Rights

RightsDeclaration (AUTHORITY = FS); (TYPE = [FS] statement #1); (ID = rulibRdec0004)
Copyright for scholarly resources published in RUcore is retained by the copyright holder. By virtue of its appearance in this open access medium, you are free to use this resource, with proper attribution, in educational and other non-commercial settings. Other uses, such as reproduction or republication, may require the permission of the copyright holder.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsEvent
Type
Permission or license
AssociatedObject
Type
License
Name
Multiple author license v. 1
Detail
I hereby grant to Rutgers, The State University of New Jersey (Rutgers) the non-exclusive right to retain, reproduce, and distribute the deposited work (Work) in whole or in part, in and from its electronic format, without fee. This agreement does not represent a transfer of copyright to Rutgers.Rutgers may make and keep more than one copy of the Work for purposes of security, backup, preservation, and access and may migrate the Work to any medium or format for the purpose of preservation and access in the future. Rutgers will not make any alteration, other than as allowed by this agreement, to the Work.I represent and warrant to Rutgers that the Work is my original work. I also represent that the Work does not, to the best of my knowledge, infringe or violate any rights of others.I further represent and warrant that I have obtained all necessary rights to permit Rutgers to reproduce and distribute the Work and that any third-party owned content is clearly identified and acknowledged within the Work.By granting this license, I acknowledge that I have read and agreed to the terms of this agreement and all related RUcore and Rutgers policies.
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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
Document
CreatingApplication
Version
1.5
ApplicationName
MiKTeX pdfTeX-1.40.21
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2020-07-04T21:13:29
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2020-07-04T21:13:29
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