Moving Object Detection Method via ResNet-18 With Encoder-Decoder Structure in Complex Scenes

In complex scenes, dynamic background, illumination variation, and shadow are important factors, which make conventional moving object detection algorithms suffer from poor performance. To solve this problem, a moving object detection method via ResNet-18 with encoder-decoder structure is proposed t...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE access Ročník 7; s. 108152 - 108160
Hlavní autori:	Ou, Xianfeng, Yan, Pengcheng, Zhang, Yiming, Tu, Bing, Zhang, Guoyun, Wu, Jianhui, Li, Wujing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms background subtraction Coders Complex scenes Convolution Decoding encoder-decoder network Encoders-Decoders Euclidean geometry Feature extraction Heuristic algorithms Illumination Image segmentation Lighting moving object detection Moving object recognition Object detection Pixels ResNet-18 Segmentation Shadows
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	In complex scenes, dynamic background, illumination variation, and shadow are important factors, which make conventional moving object detection algorithms suffer from poor performance. To solve this problem, a moving object detection method via ResNet-18 with encoder-decoder structure is proposed to segment moving objects from complex scenes. ResNet-18 with encoder-decoder structure possesses pixel-level classification capability to divide pixels into foreground and background, and it performs well in feature extraction because of its layers are so shallow that many more low-scale features will be retained. First, the object frames and their corresponding artificial labels are input to the network. Then, feature vectors will be generated by the encoder, and they are converted into segmentation maps by the decoder through deconvolution processing. Third, a rough matching of the moving object regions will be obtained, and finally, the Euclidean distance is used to match the moving object regions accurately. The proposed method is suitable for the scenes where dynamic background, illumination variation, and shadow exist, and experimental results on the public standard CDnet2014 and I2R datasets, from both qualitative and quantitative comparison aspects, demonstrate that the proposed method outperforms state-of-the-art algorithms significantly, and its mean F-measure increased by 1.99%~29.17%.
AbstractList	In complex scenes, dynamic background, illumination variation, and shadow are important factors, which make conventional moving object detection algorithms suffer from poor performance. To solve this problem, a moving object detection method via ResNet-18 with encoder-decoder structure is proposed to segment moving objects from complex scenes. ResNet-18 with encoder-decoder structure possesses pixel-level classification capability to divide pixels into foreground and background, and it performs well in feature extraction because of its layers are so shallow that many more low-scale features will be retained. First, the object frames and their corresponding artificial labels are input to the network. Then, feature vectors will be generated by the encoder, and they are converted into segmentation maps by the decoder through deconvolution processing. Third, a rough matching of the moving object regions will be obtained, and finally, the Euclidean distance is used to match the moving object regions accurately. The proposed method is suitable for the scenes where dynamic background, illumination variation, and shadow exist, and experimental results on the public standard CDnet2014 and I2R datasets, from both qualitative and quantitative comparison aspects, demonstrate that the proposed method outperforms state-of-the-art algorithms significantly, and its mean F-measure increased by 1.99%~29.17%.
Author	Yan, Pengcheng Zhang, Guoyun Li, Wujing Tu, Bing Wu, Jianhui Zhang, Yiming Ou, Xianfeng
Author_xml	– sequence: 1 givenname: Xianfeng orcidid: 0000-0003-4419-7362 surname: Ou fullname: Ou, Xianfeng organization: School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China – sequence: 2 givenname: Pengcheng surname: Yan fullname: Yan, Pengcheng organization: School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China – sequence: 3 givenname: Yiming surname: Zhang fullname: Zhang, Yiming organization: School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China – sequence: 4 givenname: Bing surname: Tu fullname: Tu, Bing email: tu_bing@163.com organization: School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China – sequence: 5 givenname: Guoyun surname: Zhang fullname: Zhang, Guoyun email: gyzhang@hnist.edu.cn organization: School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China – sequence: 6 givenname: Jianhui surname: Wu fullname: Wu, Jianhui organization: School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China – sequence: 7 givenname: Wujing surname: Li fullname: Li, Wujing organization: School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China
BookMark	eNqFUU1LJDEQDaKw6voLvAT23LOppD-So7TjB-gKOy57WkI6XdEMY2c2nRncf78ZW0S8WJdXFPXeK-odkf0hDEjIKbAZAFPfz9p2vljMOAM140qA4nyPHHKoVSEqUe-_67-Qk3Fcslwyj6rmkPy5DVs_PNC7bok20XNMGXwY6C2mx9DTrTf0J44_MBUg6W-fHul8sKHHWJzjC9JFihubNhGpH2gbntYrfKYLiwOOX8mBM6sRT17xmPy6mN-3V8XN3eV1e3ZT2JLJVDgGWLq-aUQnu6o3veXCcBCW1cyhUU4KwYVTpoNOAOcSwNRGdXXVl8I5Lo7J9aTbB7PU6-ifTPyng_H6ZRDigzYxebtC3XDmrEXjmJUlmFLZmonaApPYsaydtb5NWusY_m5wTHoZNnHI52teVlWdf16pvCWmLRvDOEZ0b67A9C4WPcWid7Ho11gyS31gWZ_M7t8pGr_6hHs6cT0ivrnJRkLTKPEfzPObrQ
CODEN	IAECCG
CitedBy_id	crossref_primary_10_1016_j_neucom_2022_07_081 crossref_primary_10_1109_TIM_2022_3165276 crossref_primary_10_1109_ACCESS_2022_3140341 crossref_primary_10_1371_journal_pone_0270376 crossref_primary_10_1016_j_yofte_2025_104226 crossref_primary_10_1016_j_procs_2022_09_223 crossref_primary_10_1016_j_energy_2022_123284 crossref_primary_10_1080_10106049_2021_1923825 crossref_primary_10_1016_j_ins_2020_11_006 crossref_primary_10_1016_j_measurement_2021_109864 crossref_primary_10_3390_photonics11010053 crossref_primary_10_1038_s41598_025_12861_3 crossref_primary_10_1049_ipr2_12767 crossref_primary_10_1007_s11042_023_15875_z crossref_primary_10_1007_s00217_025_04720_2 crossref_primary_10_1109_ACCESS_2022_3141781 crossref_primary_10_1080_21681163_2023_2261554 crossref_primary_10_3389_feart_2023_1288003 crossref_primary_10_17163_ings_n33_2025_08 crossref_primary_10_1007_s11282_024_00739_5 crossref_primary_10_1016_j_bspc_2024_107234 crossref_primary_10_1007_s00521_021_06234_w crossref_primary_10_1007_s12524_024_01994_z crossref_primary_10_3390_rs16111854 crossref_primary_10_1109_TITS_2021_3077883 crossref_primary_10_3390_app10186391 crossref_primary_10_1016_j_compgeo_2024_106518 crossref_primary_10_1109_TCSVT_2020_2991191 crossref_primary_10_3390_app10144908 crossref_primary_10_1109_ACCESS_2023_3281558 crossref_primary_10_32604_cmc_2022_022989 crossref_primary_10_3390_rs14236095 crossref_primary_10_5194_asr_20_129_2024 crossref_primary_10_1109_ACCESS_2021_3086811 crossref_primary_10_1007_s00371_023_03168_3 crossref_primary_10_1049_ipr2_12476 crossref_primary_10_1016_j_neucom_2021_08_105 crossref_primary_10_3390_brainsci11111397 crossref_primary_10_3103_S1060992X2104007X crossref_primary_10_3390_e23111537 crossref_primary_10_1007_s11042_021_11102_9 crossref_primary_10_1109_JIOT_2024_3406202 crossref_primary_10_1007_s10278_022_00629_4 crossref_primary_10_1109_ACCESS_2023_3260403 crossref_primary_10_1109_ACCESS_2020_3028740 crossref_primary_10_1371_journal_pone_0313734 crossref_primary_10_1109_TMM_2020_3006419 crossref_primary_10_3390_bioengineering10060639 crossref_primary_10_1080_10106049_2021_1948110 crossref_primary_10_3390_math13121927 crossref_primary_10_1016_j_patcog_2022_108719 crossref_primary_10_1097_APO_0000000000000576 crossref_primary_10_3390_s24186130 crossref_primary_10_7759_cureus_83548 crossref_primary_10_1049_ipr2_13010 crossref_primary_10_1109_ACCESS_2020_3032164 crossref_primary_10_1145_3596258 crossref_primary_10_1109_TCSVT_2020_3042559 crossref_primary_10_3390_s20123591 crossref_primary_10_1007_s10489_024_05325_0 crossref_primary_10_1007_s10044_023_01157_9 crossref_primary_10_1109_ACCESS_2020_2972562 crossref_primary_10_1007_s00521_025_11354_8 crossref_primary_10_1038_s41598_025_07837_2 crossref_primary_10_1109_ACCESS_2024_3491655 crossref_primary_10_3390_foods12071508 crossref_primary_10_1016_j_heliyon_2024_e31442 crossref_primary_10_1007_s13369_022_07082_z crossref_primary_10_1016_j_anucene_2024_110340 crossref_primary_10_1016_j_meatsci_2023_109281
Cites_doi	10.1109/TCSVT.2014.2358029 10.1016/j.cosrev.2016.11.001 10.5244/C.26.115 10.1109/TIP.2010.2101613 10.1109/ACCESS.2019.2915630 10.1016/j.cviu.2013.11.006 10.1016/j.cosrev.2018.03.001 10.1109/ACCESS.2018.2817129 10.1109/ICME.2010.5582601 10.1109/CVPRW.2012.6238922 10.1109/CVPRW.2014.126 10.1109/CVPR.2016.90 10.1109/TPAMI.2006.68 10.1109/PERVASIVE.2015.7087138 10.1109/TCSVT.2017.2669362 10.1109/ACCESS.2019.2908685 10.1109/TSMCB.2012.2196432 10.1109/CVPR.2018.00330 10.1109/TIP.2004.836169 10.1109/TPAMI.2012.132 10.1016/j.neuroimage.2017.04.039 10.1016/j.cosrev.2014.04.001 10.1109/ACCESS.2018.2868801 10.1109/CVPR.2012.6247916 10.1109/CVPRW.2012.6238925 10.1109/ICCV.2017.238 10.1109/TIP.2015.2419084 10.1016/j.cviu.2013.12.005 10.1109/LSP.2018.2873892 10.1109/TPAMI.2017.2717828 10.1109/TPAMI.2016.2644615 10.3969/j.issn.1004-4132.2011.04.006 10.1109/WACV.2016.7477595 10.1109/CVPR.2015.7298965 10.1109/ICPR.2004.1333992
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019
DBID	97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA
DOI	10.1109/ACCESS.2019.2931922
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present 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 DOAJ Directory of Open Access Journals
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
EISSN	2169-3536
EndPage	108160
ExternalDocumentID	oai_doaj_org_article_720fcceaf0c841a49c6036c108eb0122 10_1109_ACCESS_2019_2931922 8781779
Genre	orig-research
GrantInformation_xml	– fundername: Science and Technology Program of Hunan Province grantid: 2016TP1021 – fundername: Natural Science Foundation of Hunan Province grantid: 2019JJ40104 funderid: 10.13039/501100004735 – fundername: Hunan Provincial Innovation Foundation for Postgraduate grantid: CX2018B776; CX2018B779; YCX2019A14 funderid: 10.13039/501100010083 – fundername: National Natural Science Foundation of China grantid: 51704115 funderid: 10.13039/501100001809
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 RIG
ID	FETCH-LOGICAL-c408t-f01e4fd773b8b5dadc23a213c060fea9f83323f9ab1b3122811a6a9b65d43ff23
IEDL.DBID	DOA
ISICitedReferencesCount	84
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000481980800033&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	Tue Oct 14 19:02:21 EDT 2025 Mon Jun 30 04:42:00 EDT 2025 Tue Nov 18 20:49:28 EST 2025 Sat Nov 29 03:57:50 EST 2025 Wed Aug 27 02:54:25 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c408t-f01e4fd773b8b5dadc23a213c060fea9f83323f9ab1b3122811a6a9b65d43ff23
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-4419-7362
OpenAccessLink	https://doaj.org/article/720fcceaf0c841a49c6036c108eb0122
PQID	2455611059
PQPubID	4845423
PageCount	9
ParticipantIDs	crossref_citationtrail_10_1109_ACCESS_2019_2931922 proquest_journals_2455611059 crossref_primary_10_1109_ACCESS_2019_2931922 ieee_primary_8781779 doaj_primary_oai_doaj_org_article_720fcceaf0c841a49c6036c108eb0122
PublicationCentury	2000
PublicationDate	20190000 2019-00-00 20190101 2019-01-01
PublicationDateYYYYMMDD	2019-01-01
PublicationDate_xml	– year: 2019 text: 20190000
PublicationDecade	2010
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE access
PublicationTitleAbbrev	Access
PublicationYear	2019
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	ref35 ref13 ref34 ref12 ref15 ref36 ref14 ref31 ref30 ref33 ref11 ref32 ref10 ref2 ref39 ref17 ref38 ref16 chen (ref28) 2015 ref19 ref18 joshi (ref1) 2012; 2 xue (ref37) 2011 ref24 ref23 ref20 ref41 ref22 ref21 ioffe (ref27) 2015 garcia-garcia (ref25) 2017 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 nair (ref26) 2010
References_xml	– ident: ref6 doi: 10.1109/TCSVT.2014.2358029 – start-page: 807 year: 2010 ident: ref26 article-title: Rectified linear units improve restricted Boltzmann machines publication-title: Proc 27th Int Conf Mach Learn – ident: ref8 doi: 10.1016/j.cosrev.2016.11.001 – ident: ref39 doi: 10.5244/C.26.115 – volume: 2 start-page: 44 year: 2012 ident: ref1 article-title: A survey on moving object detection and tracking in video surveillance system publication-title: Int J Soft Comput Eng – ident: ref31 doi: 10.1109/TIP.2010.2101613 – ident: ref20 doi: 10.1109/ACCESS.2019.2915630 – ident: ref33 doi: 10.1016/j.cviu.2013.11.006 – ident: ref2 doi: 10.1016/j.cosrev.2018.03.001 – ident: ref22 doi: 10.1109/ACCESS.2018.2817129 – year: 2015 ident: ref28 article-title: MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems publication-title: arXiv 1512 01274 – ident: ref36 doi: 10.1109/ICME.2010.5582601 – ident: ref32 doi: 10.1109/CVPRW.2012.6238922 – ident: ref41 doi: 10.1109/CVPRW.2014.126 – ident: ref23 doi: 10.1109/CVPR.2016.90 – ident: ref35 doi: 10.1109/TPAMI.2006.68 – ident: ref3 doi: 10.1109/PERVASIVE.2015.7087138 – ident: ref10 doi: 10.1109/TCSVT.2017.2669362 – ident: ref12 doi: 10.1109/ACCESS.2019.2908685 – ident: ref14 doi: 10.1109/TSMCB.2012.2196432 – ident: ref19 doi: 10.1109/CVPR.2018.00330 – ident: ref40 doi: 10.1109/TIP.2004.836169 – ident: ref5 doi: 10.1109/TPAMI.2012.132 – ident: ref21 doi: 10.1016/j.neuroimage.2017.04.039 – ident: ref7 doi: 10.1016/j.cosrev.2014.04.001 – ident: ref13 doi: 10.1109/ACCESS.2018.2868801 – year: 2017 ident: ref25 article-title: A review on deep learning techniques applied to semantic segmentation publication-title: arXiv 1704 06857 – ident: ref38 doi: 10.1109/CVPR.2012.6247916 – ident: ref30 doi: 10.1109/CVPRW.2012.6238925 – ident: ref18 doi: 10.1109/ICCV.2017.238 – ident: ref34 doi: 10.1109/TIP.2015.2419084 – ident: ref9 doi: 10.1016/j.cviu.2013.12.005 – ident: ref24 doi: 10.1109/LSP.2018.2873892 – ident: ref11 doi: 10.1109/TPAMI.2017.2717828 – ident: ref16 doi: 10.1109/TPAMI.2016.2644615 – start-page: 1 year: 2011 ident: ref37 article-title: Hybrid center-symmetric local pattern for dynamic background subtraction publication-title: Proc IEEE Int Conf Multimedia Expo – ident: ref4 doi: 10.3969/j.issn.1004-4132.2011.04.006 – ident: ref17 doi: 10.1109/WACV.2016.7477595 – ident: ref15 doi: 10.1109/CVPR.2015.7298965 – ident: ref29 doi: 10.1109/ICPR.2004.1333992 – year: 2015 ident: ref27 article-title: Batch normalization: Accelerating deep network training by reducing internal covariate shift publication-title: arXiv 1502 03167
SSID	ssj0000816957
Score	2.4876647
Snippet	In complex scenes, dynamic background, illumination variation, and shadow are important factors, which make conventional moving object detection algorithms...
SourceID	doaj proquest crossref ieee
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	108152
SubjectTerms	Algorithms background subtraction Coders Complex scenes Convolution Decoding encoder-decoder network Encoders-Decoders Euclidean geometry Feature extraction Heuristic algorithms Illumination Image segmentation Lighting moving object detection Moving object recognition Object detection Pixels ResNet-18 Segmentation Shadows
SummonAdditionalLinks	– databaseName: IEEE Electronic Library (IEL) dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwEB21FQc4QKEgFkrlA8em9Vdi-1i2rXrpgiiIXpBlT2x1pSpFu2nFzyd23AgEQuopUWRHTp49Y4897wG898ygQMmryLmvpI6-MgJ9hU07WEyHzqtRbEItFvry0nzagP0pFyaEkA-fhYN0m_fy2xu8TaGyQ600U8pswqZSaszVmuIpSUDC1KoQCzFqDo_m8-Eb0uktczA4tWEqw_9wPpmjv4iq_GWJs3s5ffawhm3D0zKNJEcj7s9hI3Qv4Mlv5II78P08BwvIR59CLeQ49PnUVUfOs2g0uVs68jmsF6GvmCbflv0VOelShvuqOg75Si4yueztKpBlR5LluA4_yQUm8_gSvp6efJmfVUVMoUJJdV9FyoKMrVLCa1-3rkUuHGcCaUNjcCZqIbiIxnnmBeNcM-YaZ3xTt1LEyMUr2OpuuvAaSKOYU04NhrGmskXtpNCIkdLIa8-pnwG__8sWC9N4Ery4tnnFQY0dobEJGlugmcH-VOnHSLTx_-IfEnxT0cSSnR8MuNgy6KziNCIGFylqyZw02AwOGxnVwactxRnsJCynlxQYZ7B73xlsGdFry2USEk2z0Tf_rvUWHqcGjuGZXdgaEArv4BHe9cv1ai931l86z-eB priority: 102 providerName: IEEE
Title	Moving Object Detection Method via ResNet-18 With Encoder-Decoder Structure in Complex Scenes
URI	https://ieeexplore.ieee.org/document/8781779 https://www.proquest.com/docview/2455611059 https://doaj.org/article/720fcceaf0c841a49c6036c108eb0122
Volume	7
WOSCitedRecordID	wos000481980800033&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/eLvHCXMwrV1NT9wwELUq1EN7QLRQdYEiH3pswF-J7SNdFnHZbVVAcLPsiS1WQgHthhUnxH_gH_aX1HbCahFSe-kliiInsWcmz2PHfg-hr45q4CBYERhzhVDBFZqDK6CqI2JasE52YhNyMlGXl_rnitRXWhPW0QN3hjuQjAQAbwMBJagVGqoIukCJ8i79FkroG7OelcFUxmBFK13KnmaIEn1wOBzGFqW1XHo_dnExsWEvuqLM2N9LrLzC5dzZHG-g9T5LxIdd7T6gN775iN6vcAduIjPOcwH4h0szKfjIt3lRVYPHWRMaL6YW__LziW8LqvDFtL3CoyZtYJ_9fnw68vkMn2b22LuZx9MGJ2i49vf4FBL-baHz49HZ8KTo1RIKEES1RSDUi1BLyZ1yZW1rYNwyyoFUJHirg-Kc8aCto45HwylKbWW1q8pa8BAY_4TWmpvGf0a4ktRKKyPylUTUoKzgCiAQEljpGHEDxJ4NZ6CnEk-KFtcmDymINp21TbK26a09QN-WN912TBp_L_49eWRZNNFg5wsxOEwfHOZfwTFAm8mfy4coqaiUeoB2n_1r-k92bphISqEp3dz-H6_eQe9Sc7rZml20Fv3pv6C3sGin89lejtZ4HD-M9vKewz_2ee4Z
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwEB2VggQ98NWiLhTwgWPT-iuxfSzbVkV0F0SL6AVZ9sQWK1Up2k0rfj6xk0YgEBKnRJEdOXn2jD32vAfwxjODAiUvIue-kDr6wgj0BVZ1ZzEdOq96sQk1n-uLC_NxDXbHXJgQQj58FvbSbd7Lr6_wOoXK9rXSTClzB-6WUnLWZ2uNEZUkIWFKNVALMWr2D6bT7ivS-S2z17m1bjLDf3M_maV_kFX5wxZnB3P86P-a9hgeDhNJctAj_wTWQvMUNn6hF9yEr7McLiAffAq2kMPQ5nNXDZll2Whys3DkU1jNQ1swTb4s2m_kqEk57sviMOQrOcv0stfLQBYNSbbjMvwgZ5gM5BZ8Pj46n54Ug5xCgZLqtoiUBRlrpYTXvqxdjVw4zgTSisbgTNRCcBGN88wLxrlmzFXO-KqspYiRi2ew3lw1YRtIpZhTTnWmsaSyRu2k0IiR0shLz6mfAL_9yxYHrvEkeXFp85qDGttDYxM0doBmArtjpe891ca_i79N8I1FE092ftDhYodhZxWnETG4SFFL5qTBqnPZyKgOPm0qTmAzYTm-ZIBxAju3ncEOY3pluUxSomk--vzvtV7D_ZPz2ak9fTd__wIepMb2wZodWO_QCi_hHt60i9XyVe64PwFpsOrI
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=Moving+Object+Detection+Method+via+ResNet-18+With+Encoder%E2%80%93Decoder+Structure+in+Complex+Scenes&rft.jtitle=IEEE+access&rft.au=Ou%2C+Xianfeng&rft.au=Yan%2C+Pengcheng&rft.au=Zhang%2C+Yiming&rft.au=Tu%2C+Bing&rft.date=2019&rft.issn=2169-3536&rft.eissn=2169-3536&rft.volume=7&rft.spage=108152&rft.epage=108160&rft_id=info:doi/10.1109%2FACCESS.2019.2931922&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_ACCESS_2019_2931922
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