Close
Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/49L86LH
Repositorysid.inpe.br/sibgrapi/2023/08.16.17.13
Last Update2023:08.16.17.13.01 (UTC) davi.duarte@unesp.br
Metadata Repositorysid.inpe.br/sibgrapi/2023/08.16.17.13.01
Metadata Last Update2024:02.17.04.05.17 (UTC) administrator
DOI10.1109/SIBGRAPI59091.2023.10347173
Citation KeyPaulaSalvSilvJr:2023:SeFeEx
TitleSelf-Supervised feature extraction for video surveillance anomaly detection
FormatOn-line
Year2023
Access Date2024, May 05
Number of Files1
Size386 KiB
2. Context
Author1 de Paula, Davi Duarte
2 Salvadeo, Denis Henrique Pinheiro
3 Silva, Lucas Brito
4 Junior, Uemerson Pinheiro
Affiliation1 Institute of Geosciences and Exact Sciences, São Paulo State University
2 Institute of Geosciences and Exact Sciences, São Paulo State University
3 Institute of Geosciences and Exact Sciences, São Paulo State University
4 Institute of Geosciences and Exact Sciences, São Paulo State University
EditorClua, Esteban Walter Gonzalez
Körting, Thales Sehn
Paulovich, Fernando Vieira
Feris, Rogerio
e-Mail Addressdavi.duarte@unesp.br
Conference NameConference on Graphics, Patterns and Images, 36 (SIBGRAPI)
Conference LocationRio Grande, RS
DateNov. 06-09, 2023
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2023-08-16 17:13:01 :: davi.duarte@unesp.br -> administrator ::
2024-02-17 04:05:17 :: administrator -> davi.duarte@unesp.br :: 2023
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsvideo surveillance
anomaly detection
feature extraction
deep learning
self-supervised learning
AbstractThe recent studies on Video Surveillance Anomaly Detection focus only on the training methodology, utilizing pre-extracted feature vectors from videos. They give little attention to methodologies for feature extraction, which could enhance the final anomaly detection quality. Thus, this work presents a self-supervised methodology named Self-Supervised Object-Centric (SSOC) for extracting features from the relationship between objects in videos. To achieve this, a pretext task is employed to predict the future position and appearance of a reference object based on a set of past frames. The Deep Learning-based model used in the pretext task is then fine-tuned on Weak Supervised datasets for the downstream task, using the Multiple Instance Learning training strategy, with the goal of detecting anomalies in the videos. In the best case scenario, the results demonstrate an increase of 3.1\% in AUC on the UCF Crime dataset and an increase of 2.8\% in AUC on the CamNuvem dataset.
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 16/08/2023 14:13 1.6 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/49L86LH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/49L86LH
Languageen
Target Filedepaula-27-without-copyright.pdf
User Groupdavi.duarte@unesp.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
7. Description control
e-Mail (login)davi.duarte@unesp.br
update 


Close