Spatially Aware Fusion in 3D Convolutional Autoencoders for Video Anomaly Detection

Surveillance videos are crucial for crime prevention and public safety, yet the challenge of defining abnormal events hinders their effectiveness, limiting the applicability of supervised methods. This paper introduces an unsupervised end-to-end architecture for video anomaly detection that applies...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE access Ročník 12; s. 104770 - 104784
Hlavní autoři:	Niaz, Asim, Ul Amin, Sareer, Soomro, Shafiullah, Zia, Hamza, Nam Choi, Kwang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Anomalies Anomaly detection autoencoders Computer Science computer vision Crime prevention Datavetenskap Effectiveness Encoders-Decoders Feature extraction intelligent surveillance systems Optical flow Pedestrians Predictive models Public safety Representations Surveillance three-dimensional convolutional neural network (3DCNN) video-based abnormal event detection Videos
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Surveillance videos are crucial for crime prevention and public safety, yet the challenge of defining abnormal events hinders their effectiveness, limiting the applicability of supervised methods. This paper introduces an unsupervised end-to-end architecture for video anomaly detection that applies spatial and temporal features to identify anomalies in surveillance footage. The model employs a three-dimensional (3D) convolutional autoencoder, with an encoder-decoder structure that learns spatiotemporal representations and reconstructs the input through the latent space. Skip connections linking the encoder and decoder blocks facilitate the transfer of information across various scales of feature representations, enhancing the reconstruction process and improving the overall performance. The architecture incorporates spatial attention modules that highlight informative regions in the input, enabling improved anomaly detection. Spatial and contextual dependencies are further acquired using 3D convolutional filters. The performance of the proposed model is assessed on four benchmark datasets: UCSD Pedestrian 1, UCSD Pedestrian 2, CUHK Avenue, and ShanghaiTech. Notably, the proposed model achieves frame-based Area Under the Curve (AUC) scores of 94.6% on UCSD Ped 1, 96.7% on UCSD Ped 2, 84.7% on CUHK Avenue, and 74.8% on ShanghaiTech. These results demonstrate the state-of-the-art performance of the proposed approach, highlighting its efficacy in real-world anomaly detection scenarios.
AbstractList	Surveillance videos are crucial for crime prevention and public safety, yet the challenge of defining abnormal events hinders their effectiveness, limiting the applicability of supervised methods. This paper introduces an unsupervised end-to-end architecture for video anomaly detection that applies spatial and temporal features to identify anomalies in surveillance footage. The model employs a three-dimensional (3D) convolutional autoencoder, with an encoder-decoder structure that learns spatiotemporal representations and reconstructs the input through the latent space. Skip connections linking the encoder and decoder blocks facilitate the transfer of information across various scales of feature representations, enhancing the reconstruction process and improving the overall performance. The architecture incorporates spatial attention modules that highlight informative regions in the input, enabling improved anomaly detection. Spatial and contextual dependencies are further acquired using 3D convolutional filters. The performance of the proposed model is assessed on four benchmark datasets: UCSD Pedestrian 1, UCSD Pedestrian 2, CUHK Avenue, and ShanghaiTech. Notably, the proposed model achieves frame-based Area Under the Curve (AUC) scores of 94.6% on UCSD Ped 1, 96.7% on UCSD Ped 2, 84.7% on CUHK Avenue, and 74.8% on ShanghaiTech. These results demonstrate the state-of-the-art performance of the proposed approach, highlighting its efficacy in real-world anomaly detection scenarios.
Author	Zia, Hamza Ul Amin, Sareer Soomro, Shafiullah Nam Choi, Kwang Niaz, Asim
Author_xml	– sequence: 1 givenname: Asim orcidid: 0000-0003-3905-9774 surname: Niaz fullname: Niaz, Asim organization: Department of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea – sequence: 2 givenname: Sareer surname: Ul Amin fullname: Ul Amin, Sareer organization: Department of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea – sequence: 3 givenname: Shafiullah orcidid: 0000-0002-4318-5055 surname: Soomro fullname: Soomro, Shafiullah organization: Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden – sequence: 4 givenname: Hamza surname: Zia fullname: Zia, Hamza organization: Department of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea – sequence: 5 givenname: Kwang orcidid: 0000-0002-7420-9216 surname: Nam Choi fullname: Nam Choi, Kwang email: knchoi@cau.ac.kr organization: Department of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea
BackLink	https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-132046$$DView record from Swedish Publication Index (Linnéuniversitetet)
BookMark	eNp9kUFv1DAQhSNUJErpL4BDJM5Z7Nhx7GOUbaFSJQ4LvVoTe4y8SuPFdqj670maIhUOzMWjp_c9WfPeFmdTmLAo3lOyo5SoT13fXx0Ou5rUfMc4ayjnr4rzmgpVsYaJsxf7m-IypSNZRi5S054Xh8MJsodxfCy7B4hYXs_Jh6n0U8n2ZR-mX2Gc86LAWHZzDjiZYDGm0oVY3nmLoeymcA8Lv8eMZrW-K147GBNePr8Xxffrq2_9l-r26-ebvrutDOdtrqhzUhkFhslWOGyts8Y0dGCcUlu7gQkD1FJDUA5KkKZVpJaWA605cCIkuyhutlwb4KhP0d9DfNQBvH4SQvyhIWZvRtSEKhyoaYFbxqUdYLDK1RRVYyQQMyxZ1ZaVHvA0D3-l7f1d95Q2TrOmrCZcLP6Pm_8Uw88ZU9bHMMflSkkzIpWqFZerS20uE0NKEZ02PsN6oxzBj5oSvVaotwr1WqF-rnBh2T_snz_9n_qwUR4RXxCCslZw9hsKLqnO
CODEN	IAECCG
CitedBy_id	crossref_primary_10_1109_ACCESS_2024_3500212 crossref_primary_10_1007_s11760_024_03797_8 crossref_primary_10_1016_j_patcog_2025_111759
Cites_doi	10.1109/ACCESS.2024.3404553 10.1007/s11042-020-09406-3 10.1109/TCSVT.2018.2884203 10.1109/WACV.2017.118 10.1109/ICME.2017.8019325 10.1109/ICCV.2019.00179 10.1016/j.patrec.2022.03.004 10.1109/ICCV.2017.45 10.1109/TCSVT.2022.3221622 10.1109/CVPR42600.2020.01438 10.1016/j.patrec.2005.10.010 10.1109/ACCESS.2022.3142247 10.1109/TCSVT.2019.2962229 10.1109/TCDS.2022.3183997 10.1109/TMM.2020.2984093 10.1007/978-3-030-58555-6_20 10.1109/ACCESS.2023.3315739 10.1109/TII.2013.2255616 10.3390/math10091555 10.1109/TCSII.2022.3161049 10.1109/TCSVT.2020.3014889 10.1109/AVSS.2019.8909850 10.1109/ICCV.2013.338 10.1109/tcds.2024.3349705 10.1109/TCSVT.2022.3190539 10.1109/TCSVT.2022.3181452 10.1109/ACCESS.2024.3374383 10.1109/ACCESS.2021.3109102 10.1109/TCDS.2018.2883368 10.1016/j.cviu.2020.102920 10.1109/ICCV.2017.315 10.1109/ACCESS.2024.3380192 10.1109/CVPR.2016.213 10.32604/csse.2023.034805 10.1109/TCSVT.2013.2280061 10.1109/ICCV.2015.510 10.1109/CVPR.2018.00684 10.1109/ACCESS.2021.3126335 10.1109/CVPR.2016.86 10.1109/TCSI.2017.2758379 10.1109/CVPR.2016.70 10.1016/j.neucom.2019.08.044 10.1109/TIFS.2019.2900907 10.1145/3394171.3413887 10.1109/ICCC51575.2020.9345287 10.1109/TIP.2017.2695105 10.1109/TPAMI.2007.70738 10.1109/TCSVT.2019.2929855 10.1007/978-3-319-59081-3_23 10.1109/TII.2019.2938527 10.1007/s10489-022-03613-1 10.1109/TCSVT.2019.2892608 10.1109/TCDS.2018.2866838 10.1109/TIP.2011.2172800 10.1109/TCSII.2022.3161061 10.1109/CVPR.2019.01227
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
DBID	97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D ADTPV AGRUY AOWAS D8T D92 ZZAVC DOA
DOI	10.1109/ACCESS.2024.3435144
DatabaseName	IEEE Xplore (IEEE) IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional SwePub SWEPUB Linnéuniversitetet full text SwePub Articles SWEPUB Freely available online SWEPUB Linnéuniversitetet SwePub Articles full text Directory of Open Access Journals (DOAJ)
DatabaseTitle	CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional
DatabaseTitleList	Materials Research Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	2169-3536
EndPage	104784
ExternalDocumentID	oai_doaj_org_article_019eb1c7a4d348dbabd9f21e95c8a0cb oai_DiVA_org_lnu_132046 10_1109_ACCESS_2024_3435144 10613764
Genre	orig-research
GrantInformation_xml	– fundername: Ministry of Science and Information and Communication Technology funderid: 10.13039/100007845 – fundername: National Information Technology Industry Promotion Agency through the High Performance Computing Support Project
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D ADTPV AGRUY AOWAS D8T D92 ZZAVC
ID	FETCH-LOGICAL-c447t-1ff89c9ac3876fe7dfdcc51b3411d2fb36ca1d1c0e8b960579028d4a124a40683
IEDL.DBID	DOA
ISICitedReferencesCount	6
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001286671500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2169-3536
IngestDate	Fri Oct 03 12:45:17 EDT 2025 Tue Nov 04 16:18:05 EST 2025 Mon Jun 30 17:08:54 EDT 2025 Sat Nov 29 04:26:59 EST 2025 Tue Nov 18 22:22:56 EST 2025 Wed Aug 27 03:06:56 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by-nc-nd/4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c447t-1ff89c9ac3876fe7dfdcc51b3411d2fb36ca1d1c0e8b960579028d4a124a40683
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-3905-9774 0000-0002-7420-9216 0000-0002-4318-5055
OpenAccessLink	https://doaj.org/article/019eb1c7a4d348dbabd9f21e95c8a0cb
PQID	3089929486
PQPubID	4845423
PageCount	15
ParticipantIDs	swepub_primary_oai_DiVA_org_lnu_132046 crossref_citationtrail_10_1109_ACCESS_2024_3435144 doaj_primary_oai_doaj_org_article_019eb1c7a4d348dbabd9f21e95c8a0cb crossref_primary_10_1109_ACCESS_2024_3435144 ieee_primary_10613764 proquest_journals_3089929486
PublicationCentury	2000
PublicationDate	20240000 2024-00-00 20240101 2024 2024-01-01
PublicationDateYYYYMMDD	2024-01-01
PublicationDate_xml	– year: 2024 text: 20240000
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE access
PublicationTitleAbbrev	Access
PublicationYear	2024
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref57 ref12 ref56 ref15 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref2 ref1 ref39 ref38 Michelucci (ref25) 2022 ref24 ref23 ref26 ref20 Mathieu (ref32) 2015 ref22 ref21 ref28 ref27 ref29
References_xml	– ident: ref54 doi: 10.1109/ACCESS.2024.3404553 – ident: ref57 doi: 10.1007/s11042-020-09406-3 – ident: ref6 doi: 10.1109/TCSVT.2018.2884203 – ident: ref34 doi: 10.1109/WACV.2017.118 – ident: ref46 doi: 10.1109/ICME.2017.8019325 – ident: ref39 doi: 10.1109/ICCV.2019.00179 – ident: ref55 doi: 10.1016/j.patrec.2022.03.004 – ident: ref47 doi: 10.1109/ICCV.2017.45 – ident: ref26 doi: 10.1109/TCSVT.2022.3221622 – ident: ref48 doi: 10.1109/CVPR42600.2020.01438 – ident: ref56 doi: 10.1016/j.patrec.2005.10.010 – ident: ref53 doi: 10.1109/ACCESS.2022.3142247 – ident: ref51 doi: 10.1109/TCSVT.2019.2962229 – ident: ref13 doi: 10.1109/TCDS.2022.3183997 – ident: ref19 doi: 10.1109/TMM.2020.2984093 – ident: ref38 doi: 10.1007/978-3-030-58555-6_20 – ident: ref41 doi: 10.1109/ACCESS.2023.3315739 – ident: ref5 doi: 10.1109/TII.2013.2255616 – ident: ref16 doi: 10.3390/math10091555 – ident: ref14 doi: 10.1109/TCSII.2022.3161049 – ident: ref9 doi: 10.1109/TCSVT.2020.3014889 – ident: ref50 doi: 10.1109/AVSS.2019.8909850 – ident: ref31 doi: 10.1109/ICCV.2013.338 – ident: ref11 doi: 10.1109/tcds.2024.3349705 – ident: ref35 doi: 10.1109/TCSVT.2022.3190539 – ident: ref2 doi: 10.1109/TCSVT.2022.3181452 – ident: ref4 doi: 10.1109/ACCESS.2024.3374383 – ident: ref40 doi: 10.1109/ACCESS.2021.3109102 – ident: ref12 doi: 10.1109/TCDS.2018.2883368 – ident: ref18 doi: 10.1016/j.cviu.2020.102920 – ident: ref42 doi: 10.1109/ICCV.2017.315 – year: 2022 ident: ref25 article-title: An introduction to autoencoders publication-title: arXiv:2201.03898 – ident: ref3 doi: 10.1109/ACCESS.2024.3380192 – ident: ref22 doi: 10.1109/CVPR.2016.213 – ident: ref58 doi: 10.32604/csse.2023.034805 – ident: ref17 doi: 10.1109/TCSVT.2013.2280061 – ident: ref27 doi: 10.1109/ICCV.2015.510 – ident: ref33 doi: 10.1109/CVPR.2018.00684 – ident: ref52 doi: 10.1109/ACCESS.2021.3126335 – ident: ref23 doi: 10.1109/CVPR.2016.86 – ident: ref8 doi: 10.1109/TCSI.2017.2758379 – ident: ref30 doi: 10.1109/CVPR.2016.70 – ident: ref37 doi: 10.1016/j.neucom.2019.08.044 – ident: ref43 doi: 10.1109/TIFS.2019.2900907 – ident: ref45 doi: 10.1145/3394171.3413887 – ident: ref49 doi: 10.1109/ICCC51575.2020.9345287 – ident: ref21 doi: 10.1109/TIP.2017.2695105 – ident: ref28 doi: 10.1109/TPAMI.2007.70738 – ident: ref1 doi: 10.1109/TCSVT.2019.2929855 – year: 2015 ident: ref32 article-title: Deep multi-scale video prediction beyond mean square error publication-title: arXiv:1511.05440 – ident: ref24 doi: 10.1007/978-3-319-59081-3_23 – ident: ref36 doi: 10.1109/TII.2019.2938527 – ident: ref20 doi: 10.1007/s10489-022-03613-1 – ident: ref7 doi: 10.1109/TCSVT.2019.2892608 – ident: ref10 doi: 10.1109/TCDS.2018.2866838 – ident: ref29 doi: 10.1109/TIP.2011.2172800 – ident: ref15 doi: 10.1109/TCSII.2022.3161061 – ident: ref44 doi: 10.1109/CVPR.2019.01227
SSID	ssj0000816957
Score	2.377203
Snippet	Surveillance videos are crucial for crime prevention and public safety, yet the challenge of defining abnormal events hinders their effectiveness, limiting the...
SourceID	doaj swepub proquest crossref ieee
SourceType	Open Website Open Access Repository Aggregation Database Enrichment Source Index Database Publisher
StartPage	104770
SubjectTerms	Anomalies Anomaly detection autoencoders Computer Science computer vision Crime prevention Datavetenskap Effectiveness Encoders-Decoders Feature extraction intelligent surveillance systems Optical flow Pedestrians Predictive models Public safety Representations Surveillance three-dimensional convolutional neural network (3DCNN) video-based abnormal event detection Videos
SummonAdditionalLinks	– databaseName: IEEE Electronic Library (IEL) dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELZoxQEOPIsIFOQD4kTaJHbi-Bh2WXGqkICqN8tPaaUlQdukqP--M4672j2AxC2y_Mw39sz48Q0hHyDdNb7SeYAlMueW87z1ps4b5kURLCgUZ2KwCXFx0V5dyW_psXp8C-O9j5fP_Bl-xrN8N9gJt8rO0X2BCcGPyJEQYn6stdtQwQgSshaJWags5Hm3WMAgwAes-BnjeGWdH2ifSNKfoqocGpj7pKFR0aye_mcXn5EnyaKk3SwCz8kD378gj_d4Bl-S7xh4GARtc0u7P3rr6WrCTTK67ilb0sXQ3yQBxHqmcUByS7zgTMGipZdr5wfa9cMvDeWXfoyXt_oT8nP15cfia56iKeQAgBjzMoRWWqktgwUweOGCs7YuDWBUuioY1lhdutIWvjXg1tQCeV0c12AAaND6LXtFjvuh968JLa2pNGONlOBeajDxHAtF6aVuZLDCsIxU939Z2UQ1jhEvNiq6HIVUMzQKoVEJmox82hX6PTNt_Dv7Z4RvlxVpsmMCwKHSrFNgv4IuskJzx3jrjDZOhgp6WttWF9Zk5AQh3GtvRi8jp_fSoNKcvlYMT0grydsmIx9nCTlofbm-7GLrm35S-DKdN2_-Uv9b8gjHMm_lnJLjcTv5d-ShvRnX19v3Ua7vAFb59mw priority: 102 providerName: IEEE
Title	Spatially Aware Fusion in 3D Convolutional Autoencoders for Video Anomaly Detection
URI	https://ieeexplore.ieee.org/document/10613764 https://www.proquest.com/docview/3089929486 https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-132046 https://doaj.org/article/019eb1c7a4d348dbabd9f21e95c8a0cb
Volume	12
WOSCitedRecordID	wos001286671500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQxQEOiEcRC6XyAXEiNI4dP45htysuVEhA1Zvlp7TSkqBttogLv52x41bZC1y45GD5Ec9M5uGMv0HoDbR7HhpTRVCRFXOMVTLYtuI0iDo6MCje5mIT4uJCXl2pz7NSXyknbIIHngh3Bi4IqBMnDPOUSW-N9So2JKjWSVM7m7QveD2zYCrrYEm4akWBGSK1OuuWS9gRBIQNe09Zyl9nB6YoI_aXEiuH3uYcQTRbnfVj9Ki4i7ibXvMJuhf6p-jhDETwGfqSqgqDFG1_4e6n2QW83qcTMLzpMV3h5dDfFOlK8-zHISFXpuxlDO4qvtz4MOCuH74bGL8KY87M6o_Rt_X51-XHqpRKqIC6YqxIjFI5ZRwF7RaD8NE71xILDCC-iZZyZ4gnrg7SQszSigTa4pkB627ApEv6HB31Qx9eIEycbQylXCmIHQ34b57GGuhtuIpOWLpAzS3VtCs44qmcxVbneKJWeiK1TqTWhdQL9O5u0I8JRuPv3T8kdtx1TRjYuQEkQxfJ0P-SjAU6TsycrQeui-Aw-cktd3X5YK81Tb8_G8UkX6C3E8cPVl9tLru8-rbf63TtnPGX_-MlX6EHaePToc4JOhp3-_Aa3Xc34-Z6d5qFGp6ffp-f5quJfwC0kfx8
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB5BQQIOPIsaKOAD4kTaJHYePi67rIooKyRK1Zvlp7TSNqm2SRH_nrHjrnYPIHGLLD_zjT0zfnwD8B7TTWULmTpcIlOmGUsbq8q0orbOnEaFYlQINlEvFs3FBf8eH6uHtzDW2nD5zB75z3CWbzo9-K2yY---4IRgd-FeyViRj8-1NlsqPoYEL-vILZRn_HgyneIw0Ass2BFl_tI629E_gaY_xlXZNTG3aUODqpk_-c9OPoXH0aYkk1EInsEd2z6HR1tMgy_ghw89jKK2-k0mv-Takvngt8nIsiV0RqZdexNF0Ncz9J2nt_RXnAnatOR8aWxHJm13KbH8zPbh-la7Dz_nn8-mJ2mMp5AiBHWf5s41XHOpKS6BztbGGa3LXCFKuSmcopWWucl1ZhuFjk1Ze2YXwySaABL1fkNfwl7btfYASK5VISmtOEcHU6KRZ6jLcstlxZ2uFU2guP3LQkeycR_zYiWC05FxMUIjPDQiQpPAx02hq5Fr49_ZP3n4Nlk9UXZIQDhEnHcCLVjURrqWzFDWGCWV4a7Anpa6kZlWCex7CLfaG9FL4PBWGkSc1deC-jPSgrOmSuDDKCE7rc-W55PQ-qodhH-bzqpXf6n_HTw4Oft2Kk6_LL6-hod-XOPGziHs9evBvoH7-qZfXq_fBhn_A0ka-bM
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Spatially+Aware+Fusion+in+3D+Convolutional+Autoencoders+for+Video+Anomaly+Detection&rft.jtitle=IEEE+access&rft.au=Niaz%2C+Asim&rft.au=Ul+Amin%2C+Sareer&rft.au=Soomro%2C+Shafiullah&rft.au=Zia%2C+Hamza&rft.date=2024&rft.issn=2169-3536&rft.eissn=2169-3536&rft.volume=12&rft.spage=104770&rft_id=info:doi/10.1109%2FACCESS.2024.3435144&rft.externalDocID=oai_DiVA_org_lnu_132046
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon