Abnormal event detection in video using motion and appearance information

Descripción del Articulo

This work was supported by grant 011-2013-FONDECYT (Master Program) from the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU).
Detalles Bibliográficos
Autores: Menejes Palomino N., Cámara Chávez G.
Formato: objeto de conferencia
Fecha de Publicación:2018
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/506
Enlace del recurso:https://hdl.handle.net/20.500.12390/506
https://doi.org/10.1007/978-3-319-75193-1_46
Nivel de acceso:acceso abierto
Materia:Video surveillance
Computer vision
Feature extraction
Information use
Motion analysis
Optical flows
Security systems
Abnormal event detections
Detection accuracy
Detection and localization
Motion information
State-of-the-art methods
Video analysis
Pattern recognition
https://purl.org/pe-repo/ocde/ford#1.02.01
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oai_identifier_str oai:repositorio.concytec.gob.pe:20.500.12390/506
network_acronym_str CONC
network_name_str CONCYTEC-Institucional
repository_id_str 4689
dc.title.none.fl_str_mv Abnormal event detection in video using motion and appearance information
title Abnormal event detection in video using motion and appearance information
spellingShingle Abnormal event detection in video using motion and appearance information
Menejes Palomino N.
Video surveillance
Computer vision
Feature extraction
Information use
Motion analysis
Optical flows
Security systems
Abnormal event detections
Detection accuracy
Detection and localization
Motion information
State-of-the-art methods
Video analysis
Pattern recognition
https://purl.org/pe-repo/ocde/ford#1.02.01
title_short Abnormal event detection in video using motion and appearance information
title_full Abnormal event detection in video using motion and appearance information
title_fullStr Abnormal event detection in video using motion and appearance information
title_full_unstemmed Abnormal event detection in video using motion and appearance information
title_sort Abnormal event detection in video using motion and appearance information
author Menejes Palomino N.
author_facet Menejes Palomino N.
Cámara Chávez G.
author_role author
author2 Cámara Chávez G.
author2_role author
dc.contributor.author.fl_str_mv Menejes Palomino N.
Cámara Chávez G.
dc.subject.none.fl_str_mv Video surveillance
topic Video surveillance
Computer vision
Feature extraction
Information use
Motion analysis
Optical flows
Security systems
Abnormal event detections
Detection accuracy
Detection and localization
Motion information
State-of-the-art methods
Video analysis
Pattern recognition
https://purl.org/pe-repo/ocde/ford#1.02.01
dc.subject.es_PE.fl_str_mv Computer vision
Feature extraction
Information use
Motion analysis
Optical flows
Security systems
Abnormal event detections
Detection accuracy
Detection and localization
Motion information
State-of-the-art methods
Video analysis
Pattern recognition
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#1.02.01
description This work was supported by grant 011-2013-FONDECYT (Master Program) from the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU).
publishDate 2018
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.isbn.none.fl_str_mv 9783319751924
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/506
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1007/978-3-319-75193-1_46
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85042233597
identifier_str_mv 9783319751924
2-s2.0-85042233597
url https://hdl.handle.net/20.500.12390/506
https://doi.org/10.1007/978-3-319-75193-1_46
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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spelling Publicationrp00668600rp00667600Menejes Palomino N.Cámara Chávez G.2024-05-30T23:13:38Z2024-05-30T23:13:38Z20189783319751924https://hdl.handle.net/20.500.12390/506https://doi.org/10.1007/978-3-319-75193-1_462-s2.0-85042233597This work was supported by grant 011-2013-FONDECYT (Master Program) from the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU).This paper presents an approach for the detection and localization of abnormal events in pedestrian areas. The goal is to design a model to detect abnormal events in video sequences using motion and appearance information. Motion information is represented through the use of the velocity and acceleration of optical flow and the appearance information is represented by texture and optical flow gradient. Unlike literature methods, our proposed approach provides a general solution to detect both global and local abnormal events. Furthermore, in the detection stage, we propose a classification by local regions. Experimental results on UMN and UCSD datasets confirm that the detection accuracy of our method is comparable to state-of-the-art methods.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengSpringer VerlagLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccessVideo surveillanceComputer vision-1Feature extraction-1Information use-1Motion analysis-1Optical flows-1Security systems-1Abnormal event detections-1Detection accuracy-1Detection and localization-1Motion information-1State-of-the-art methods-1Video analysis-1Pattern recognition-1https://purl.org/pe-repo/ocde/ford#1.02.01-1Abnormal event detection in video using motion and appearance informationinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/506oai:repositorio.concytec.gob.pe:20.500.12390/5062024-05-30 15:35:37.021http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="dacfa8d7-75a9-4d13-b548-c8d09639440e"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Abnormal event detection in video using motion and appearance information</Title> <PublishedIn> <Publication> <Title>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</Title> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <DOI>https://doi.org/10.1007/978-3-319-75193-1_46</DOI> <SCP-Number>2-s2.0-85042233597</SCP-Number> <ISBN>9783319751924</ISBN> <Authors> <Author> <DisplayName>Menejes Palomino N.</DisplayName> <Person id="rp00668" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Cámara Chávez G.</DisplayName> <Person id="rp00667" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Springer Verlag</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Video surveillance</Keyword> <Keyword>Computer vision</Keyword> <Keyword>Feature extraction</Keyword> <Keyword>Information use</Keyword> <Keyword>Motion analysis</Keyword> <Keyword>Optical flows</Keyword> <Keyword>Security systems</Keyword> <Keyword>Abnormal event detections</Keyword> <Keyword>Detection accuracy</Keyword> <Keyword>Detection and localization</Keyword> <Keyword>Motion information</Keyword> <Keyword>State-of-the-art methods</Keyword> <Keyword>Video analysis</Keyword> <Keyword>Pattern recognition</Keyword> <Abstract>This paper presents an approach for the detection and localization of abnormal events in pedestrian areas. The goal is to design a model to detect abnormal events in video sequences using motion and appearance information. Motion information is represented through the use of the velocity and acceleration of optical flow and the appearance information is represented by texture and optical flow gradient. Unlike literature methods, our proposed approach provides a general solution to detect both global and local abnormal events. Furthermore, in the detection stage, we propose a classification by local regions. Experimental results on UMN and UCSD datasets confirm that the detection accuracy of our method is comparable to state-of-the-art methods.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
score 13.210282
Abnormal event detection in video using motion and appearance information
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).

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