Extracting Moving Objects More Accurately: A CDA Contour Optimizer

In the area of change detection, there were a rare number of optimization methods. Most of the optimization methods that are used by change detection are morphological transformation or median filtering, which cannot best optimize change detection algorithm. In this paper, a general post-processing...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on circuits and systems for video technology Ročník 31; číslo 12; s. 4840 - 4849
Hlavní autoři:	Gao, Fei, Li, Yunyang, Lu, Shufang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Change detection change detection algorithm Change detection algorithms Contours Edge detection Feature extraction Image edge detection motion detection Moving object recognition Optical filters Optimization optimization method Post-processing algorithm Semantics Supervised learning Unsupervised learning
ISSN:	1051-8215, 1558-2205
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	In the area of change detection, there were a rare number of optimization methods. Most of the optimization methods that are used by change detection are morphological transformation or median filtering, which cannot best optimize change detection algorithm. In this paper, a general post-processing algorithm for change detection is proposed. We believe that some problems cannot be avoided in the area of change detection such as 1) region of moving object generated by change detection is slightly larger than the ground-truth and 2) there are always some disjoint and small regions that are independent from the moving objects. To address the problem, our method can optimize the change detection algorithm bases on the idea of edge detection, which can remove the wrong edge or pixel. In the experiments, more than 20 change detection algorithms that include the best algorithm in ChangeDetection.net are selected. Most of these change detection algorithms are optimized by the proposed method on PWC, Precision, and FMeasure, where, our optimized algorithm named FgSegNet_v2 is better than all other algorithms in the CDnet. The best-optimized margin of PWC is 0.64, and the fast speed is 548FPS on CPU. Our approach can better resolve the afore-mentioned problems that cannot be avoided and is general and fast. The experiments can be reproduced with C++ on Github https://github.com/walty19950301/CDA-contour-optimizer .
AbstractList	In the area of change detection, there were a rare number of optimization methods. Most of the optimization methods that are used by change detection are morphological transformation or median filtering, which cannot best optimize change detection algorithm. In this paper, a general post-processing algorithm for change detection is proposed. We believe that some problems cannot be avoided in the area of change detection such as 1) region of moving object generated by change detection is slightly larger than the ground-truth and 2) there are always some disjoint and small regions that are independent from the moving objects. To address the problem, our method can optimize the change detection algorithm bases on the idea of edge detection, which can remove the wrong edge or pixel. In the experiments, more than 20 change detection algorithms that include the best algorithm in ChangeDetection.net are selected. Most of these change detection algorithms are optimized by the proposed method on PWC, Precision, and FMeasure, where, our optimized algorithm named FgSegNet_v2 is better than all other algorithms in the CDnet. The best-optimized margin of PWC is 0.64, and the fast speed is 548FPS on CPU. Our approach can better resolve the afore-mentioned problems that cannot be avoided and is general and fast. The experiments can be reproduced with C++ on Github https://github.com/walty19950301/CDA-contour-optimizer .
Author	Gao, Fei Li, Yunyang Lu, Shufang
Author_xml	– sequence: 1 givenname: Fei orcidid: 0000-0002-4678-1936 surname: Gao fullname: Gao, Fei email: feig@zjut.edu.cn organization: Laboratory of Graphics and Image Processing, College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China – sequence: 2 givenname: Yunyang surname: Li fullname: Li, Yunyang email: yunyang_li@qq.com organization: Laboratory of Graphics and Image Processing, College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China – sequence: 3 givenname: Shufang orcidid: 0000-0002-8711-1605 surname: Lu fullname: Lu, Shufang email: sflu@zjut.edu.cn organization: Laboratory of Graphics and Image Processing, College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China
BookMark	eNp9UE1PwzAMjdCQ2AZ_AC6VOHc4SdOk3EYZH9LQDgyuURq5KNPWjjRDjF9PxiYOHJBl2Zbe87PfgPSatkFCzimMKIXial4-v85HDBgdcRBC8OKI9KkQKmUMRC_2IGiqGBUnZNB1CwCaqUz2yc3kM3hjg2vekqf2Y1dm1QJt6OLoMRlbu_Em4HJ7nYyT8jZm24R245PZOriV-0J_So5rs-zw7FCH5OVuMi8f0uns_rEcT1PLChHSWlTIKYKojBWS2wyV5FmeCQMgc1bXCqlklFrIigKNLDi3taoQlZK5oTUfksv93rVv3zfYBb2IdzRRUrMchIScAY8otkdZ33adx1qvvVsZv9UU9M4r_eOV3nmlD15FkvpDsi6Y4OKr3rjl_9SLPdUh4q9WwTOIwb8BqJp3wg
CODEN	ITCTEM
CitedBy_id	crossref_primary_10_1016_j_compeleceng_2023_108671 crossref_primary_10_1109_TIP_2024_3378473 crossref_primary_10_1007_s11063_022_11092_1 crossref_primary_10_1016_j_knosys_2024_111765 crossref_primary_10_1109_TETCI_2025_3529893 crossref_primary_10_1007_s10489_022_04062_6 crossref_primary_10_1109_ACCESS_2023_3299872 crossref_primary_10_1016_j_neucom_2022_06_104 crossref_primary_10_1109_TMM_2023_3266608
Cites_doi	10.1109/ICPR.2004.1333992 10.1109/WACV.2015.137 10.1007/s11042-010-0575-2 10.1016/j.neucom.2015.04.118 10.1109/TIP.2010.2101613 10.1109/TCSVT.2016.2614984 10.1109/2.410146 10.1109/TIP.2017.2695882 10.1109/CVPR.1999.784637 10.1109/CVPRW.2014.68 10.1109/BRICS-CCI-CBIC.2013.37 10.1007/s10044-019-00845-9 10.1016/j.patrec.2016.09.014 10.1109/ICIP.2015.7351664 10.1016/j.patcog.2014.10.020 10.1109/ICPR.2010.498 10.1186/s41074-017-0036-1 10.1109/TIP.2014.2378053 10.1109/CVPRW.2014.66 10.1109/ICIP.2014.7025661 10.3390/sym11050621 10.1016/j.patcog.2017.09.040 10.1109/AVSS.2013.6636617 10.1109/CVPR.2004.1315249 10.1109/TEVC.2017.2694160 10.1109/ISCAS.2017.8050570 10.1109/TIP.2016.2598691 10.4304/jmm.2.4.20-33 10.1109/ICIP.2011.6115731 10.1109/CVPRW.2012.6238922 10.1109/ICIP.2017.8297144 10.1109/TCSVT.2015.2424052 10.1109/WIAMIS.2008.60 10.1016/j.patrec.2018.08.002 10.1016/j.neucom.2019.04.088 10.1109/TCSVT.2014.2355695 10.1109/CVPRW.2014.126 10.1109/ICSS.2014.11 10.1109/TCSVT.2013.2291358 10.1145/1291233.1291254 10.1109/CRV.2013.29 10.1109/TCSVT.2018.2795657
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
DBID	97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D
DOI	10.1109/TCSVT.2021.3055539
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef 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 Computer and Information Systems Abstracts Professional
DatabaseTitleList	Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1558-2205
EndPage	4849
ExternalDocumentID	10_1109_TCSVT_2021_3055539 9340404
Genre	orig-research
GrantInformation_xml	– fundername: National Key Research and Development Project of China grantid: 2020AAA0104001 – fundername: Zhejiang Provincial Science and Technology Planning Key Project of China grantid: 2021C03129 – fundername: Zhejiang Laboratory grantid: 2019KD0AD011005
GroupedDBID	-~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ H~9 ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS RXW TAE TN5 VH1 AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c295t-f5be31e05bac573c4e8734645a00762ff8e17211c0499ea7933cf8bee8876a1f3
IEDL.DBID	RIE
ISICitedReferencesCount	10
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000725812500026&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1051-8215
IngestDate	Sun Jun 29 16:46:28 EDT 2025 Tue Nov 18 22:35:34 EST 2025 Sat Nov 29 01:44:15 EST 2025 Wed Aug 27 05:11:51 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	12
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c295t-f5be31e05bac573c4e8734645a00762ff8e17211c0499ea7933cf8bee8876a1f3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-4678-1936 0000-0002-8711-1605
PQID	2605706203
PQPubID	85433
PageCount	10
ParticipantIDs	ieee_primary_9340404 proquest_journals_2605706203 crossref_citationtrail_10_1109_TCSVT_2021_3055539 crossref_primary_10_1109_TCSVT_2021_3055539
PublicationCentury	2000
PublicationDate	2021-12-01
PublicationDateYYYYMMDD	2021-12-01
PublicationDate_xml	– month: 12 year: 2021 text: 2021-12-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on circuits and systems for video technology
PublicationTitleAbbrev	TCSVT
PublicationYear	2021
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 ref37 ref15 ref36 ref14 ref31 ref30 ref33 ref11 ref32 ref10 ref1 ref39 ref17 ref38 ref16 ref19 ref18 wang (ref27) 2013 ref46 ref24 ref45 ref23 ref26 ref25 ref20 ref42 ref22 ref44 ref21 ref43 ref28 sav (ref2) 2005 ref29 ref8 ref7 ref9 ref4 benezeth (ref41) 2010; 19 ref3 ref6 ref5 ref40
References_xml	– ident: ref32 doi: 10.1109/ICPR.2004.1333992 – ident: ref19 doi: 10.1109/WACV.2015.137 – start-page: 475 year: 2013 ident: ref27 article-title: The optimized filter width using in post-processing ESWAN data of prostate publication-title: Proc ICME Int Conf Complex Med Eng – ident: ref5 doi: 10.1007/s11042-010-0575-2 – ident: ref43 doi: 10.1016/j.neucom.2015.04.118 – ident: ref16 doi: 10.1109/TIP.2010.2101613 – ident: ref6 doi: 10.1109/TCSVT.2016.2614984 – ident: ref1 doi: 10.1109/2.410146 – ident: ref37 doi: 10.1109/TIP.2017.2695882 – ident: ref15 doi: 10.1109/CVPR.1999.784637 – ident: ref20 doi: 10.1109/CVPRW.2014.68 – ident: ref44 doi: 10.1109/BRICS-CCI-CBIC.2013.37 – ident: ref11 doi: 10.1007/s10044-019-00845-9 – ident: ref9 doi: 10.1016/j.patrec.2016.09.014 – ident: ref38 doi: 10.1109/ICIP.2015.7351664 – ident: ref34 doi: 10.1016/j.patcog.2014.10.020 – ident: ref29 doi: 10.1109/ICPR.2010.498 – ident: ref35 doi: 10.1186/s41074-017-0036-1 – ident: ref17 doi: 10.1109/TIP.2014.2378053 – ident: ref40 doi: 10.1109/CVPRW.2014.66 – ident: ref33 doi: 10.1109/ICIP.2014.7025661 – ident: ref45 doi: 10.3390/sym11050621 – ident: ref12 doi: 10.1016/j.patcog.2017.09.040 – ident: ref42 doi: 10.1109/AVSS.2013.6636617 – ident: ref7 doi: 10.1109/CVPR.2004.1315249 – ident: ref22 doi: 10.1109/TEVC.2017.2694160 – ident: ref30 doi: 10.1109/ISCAS.2017.8050570 – ident: ref18 doi: 10.1109/TIP.2016.2598691 – ident: ref21 doi: 10.4304/jmm.2.4.20-33 – ident: ref28 doi: 10.1109/ICIP.2011.6115731 – ident: ref39 doi: 10.1109/CVPR.1999.784637 – ident: ref36 doi: 10.1109/CVPRW.2012.6238922 – ident: ref13 doi: 10.1109/ICIP.2017.8297144 – volume: 19 start-page: 1 year: 2010 ident: ref41 article-title: Comparative study of background subtraction algorithms publication-title: J Elec Imag – ident: ref23 doi: 10.1109/TCSVT.2015.2424052 – ident: ref25 doi: 10.1109/WIAMIS.2008.60 – ident: ref10 doi: 10.1016/j.patrec.2018.08.002 – ident: ref46 doi: 10.1016/j.neucom.2019.04.088 – ident: ref3 doi: 10.1109/TCSVT.2014.2355695 – start-page: 1 year: 2005 ident: ref2 article-title: Associating low-level features with semantic concepts using video objects and relevance feedback publication-title: Proc 8th Int Workshop Image Analy Multimedia Interactive Services (WIAMIS) – ident: ref8 doi: 10.1109/CVPRW.2014.126 – ident: ref26 doi: 10.1109/ICSS.2014.11 – ident: ref31 doi: 10.1109/TCSVT.2013.2291358 – ident: ref4 doi: 10.1145/1291233.1291254 – ident: ref24 doi: 10.1109/CRV.2013.29 – ident: ref14 doi: 10.1109/TCSVT.2018.2795657
SSID	ssj0014847
Score	2.4206762
Snippet	In the area of change detection, there were a rare number of optimization methods. Most of the optimization methods that are used by change detection are...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	4840
SubjectTerms	Algorithms Change detection change detection algorithm Change detection algorithms Contours Edge detection Feature extraction Image edge detection motion detection Moving object recognition Optical filters Optimization optimization method Post-processing algorithm Semantics Supervised learning Unsupervised learning
Title	Extracting Moving Objects More Accurately: A CDA Contour Optimizer
URI	https://ieeexplore.ieee.org/document/9340404 https://www.proquest.com/docview/2605706203
Volume	31
WOSCitedRecordID	wos000725812500026&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: PRVIEE databaseName: IEEE Xplore customDbUrl: eissn: 1558-2205 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014847 issn: 1051-8215 databaseCode: RIE dateStart: 19910101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFH9sw4Me_JridEoP3rRb27RN623OiQfdBOfYrTTpqwxmJ10r6l9vknZloAgeCi0kj_KSvJf39XsA564RYUwjQw8p47rNCNGZFVE9iikLPaEDXZXtPrmnw6E3nfqPNbisamEQUSWfYUe-qlh-tOC5dJV1fWKLPWfXoU6pW9RqVRED21PNxMR1wdQ9ocdWBTKG3x33nyZjYQpaZkfhW8nG4GtKSHVV-SGKlX653fnfn-3CdnmP1HrFwu9BDZN92FpDF2zC9eAjUzVQyYv2oBwH2ohJt8tSfKao9TjPJVDE_PNK62n9G_FIMIM81UZCjrzOvjA9gOfbwbh_p5ctE3Ru-U6mxw5DYqLhsJA7lHAbPUpk8DKUITcrjj1UNh-Xlg6G4nASHnsMUcgaNzRjcgiNZJHgEWhUEBDUTC-MJYac6XOTuRF1LGZIIyVsgbniYcBLPHHZ1mIeKLvC8APF90DyPSj53oKLas5bgabx5-im5HQ1smRyC9qrpQrKA7cMpFlGDdcyyPHvs05gU9IuMlHa0MjSHE9hg79ns2V6pvbSN6mlxH8
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED_mFNQHv6Y4ndoH37Qubdqm9W3OjYlzCs7hW2nSqwzmJl0n6l9vknVjoAg-FFpI0nBJ7nJfvwM49UiMCYuJGTEuTIdTanI7ZmacMB75UgZ6Otq912adjv_8HDwU4HyeC4OIOvgML9Sr9uXHIzFRprJqQB2555wlWHYdxybTbK25z8DxdTkxeWGwTF9KslmKDAmq3fpjryuVQdu60AhXqjT4ghjSdVV-MGMtYZqb_5vbFmzkN0mjNl36bSjgcAfWF_AFS3DV-Mh0FtTwxbjTpgPjnivDy1h-pmjUhJgoqIjB56VRM-rX8lFwBpPUuJec5LX_hekuPDUb3XrLzIsmmMIO3MxMXI7UQuLySLiMCgd9RpX7MlJONztJfNRan1C6DkbyeFKR-BxRchsvshK6B8XhaIj7YDA5gBzN8qNEochZgbC4FzPX5kSpKVEZrBkNQ5EjiqvCFoNQaxYkCDXdQ0X3MKd7Gc7mfd6meBp_ti4pSs9b5kQuQ2W2VGF-5MahUswY8WxCD37vdQKrre5dO2zfdG4PYU39ZxqXUoFilk7wCFbEe9Yfp8d6X30DEPrHxg
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=Extracting+Moving+Objects+More+Accurately%3A+A+CDA+Contour+Optimizer&rft.jtitle=IEEE+transactions+on+circuits+and+systems+for+video+technology&rft.au=Gao%2C+Fei&rft.au=Li%2C+Yunyang&rft.au=Lu%2C+Shufang&rft.date=2021-12-01&rft.issn=1051-8215&rft.eissn=1558-2205&rft.volume=31&rft.issue=12&rft.spage=4840&rft.epage=4849&rft_id=info:doi/10.1109%2FTCSVT.2021.3055539&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TCSVT_2021_3055539
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1051-8215&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1051-8215&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1051-8215&client=summon