Object Tracking with Channel Group Regularization and Smooth Constraints Using Improved Dynamic Convolution Kernels in ITS

Aiming at the problem that the correlation between multi-channel feature representation and filter structure is not considered in the objective function modeling, a object tracking algorithm with channel group regularization and time series smooth constraint using improved dynamic convolution kernel...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Multimedia tools and applications Jg. 84; H. 29; S. 35191 - 35215
Hauptverfasser:	Sun, Jinping, Li, Dan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.09.2025 Springer Nature B.V
Schlagworte:	1231: IoT-driven Computer Vision Technology for Smart Transportation Applications Accuracy Algorithms Artificial neural networks Channels Computer Communication Networks Computer Science Constraints Correlation Data Structures and Information Theory Feature maps Intelligent transportation systems Localization Multimedia Information Systems Neural networks Object recognition Process controls Regularization Spatial data Special Purpose and Application-Based Systems Task complexity Tracking Vision systems Time series smoothing Sparse representation Correlation filter Object tracking Dynamic convolution kernel
ISSN:	1573-7721, 1380-7501, 1573-7721
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Aiming at the problem that the correlation between multi-channel feature representation and filter structure is not considered in the objective function modeling, a object tracking algorithm with channel group regularization and time series smooth constraint using improved dynamic convolution kernels is proposed. Firstly, the elements in the filter are grouped using spatial and channel properties, the time-domain correlation of the object model is constrained by the low-rank kernel norm. A group regularization term is constructed to describe the correlation between channels with a mixed-norm structured sparsity constraint to learn lower-dimensional filters. Then, after extracting some channels in the feature map, part of the spatial information of each channel is retained to generate an efficient dynamic convolution kernel through the channel information de-redundant attention mechanism to obtain an optimized lightweight convolutional neural network. Finally, combining the advantages of hand-crafted features and deep convolutional features, the complementary localization of the object from coarse to fine is realized with the help of the constructed efficient feature model and the learned filter. The experimental results on public datasets show that the proposed algorithm can adapt to the tracking tasks of various complex traffic scenes, and enhance the tracking performance of the existing models. The proposed algorithm improves the discriminative property of the object model and the self-adaptability of the spatio-temporal information of the dynamic convolution kernel, and can be applied to neuromorphic vision system of intelligent transportation systems.
AbstractList	Aiming at the problem that the correlation between multi-channel feature representation and filter structure is not considered in the objective function modeling, a object tracking algorithm with channel group regularization and time series smooth constraint using improved dynamic convolution kernels is proposed. Firstly, the elements in the filter are grouped using spatial and channel properties, the time-domain correlation of the object model is constrained by the low-rank kernel norm. A group regularization term is constructed to describe the correlation between channels with a mixed-norm structured sparsity constraint to learn lower-dimensional filters. Then, after extracting some channels in the feature map, part of the spatial information of each channel is retained to generate an efficient dynamic convolution kernel through the channel information de-redundant attention mechanism to obtain an optimized lightweight convolutional neural network. Finally, combining the advantages of hand-crafted features and deep convolutional features, the complementary localization of the object from coarse to fine is realized with the help of the constructed efficient feature model and the learned filter. The experimental results on public datasets show that the proposed algorithm can adapt to the tracking tasks of various complex traffic scenes, and enhance the tracking performance of the existing models. The proposed algorithm improves the discriminative property of the object model and the self-adaptability of the spatio-temporal information of the dynamic convolution kernel, and can be applied to neuromorphic vision system of intelligent transportation systems.
Author	Li, Dan Sun, Jinping
Author_xml	– sequence: 1 givenname: Jinping surname: Sun fullname: Sun, Jinping email: sjp@xzit.edu.cn organization: School of Information Engineering (School of Big Data), Xuzhou University of Technology – sequence: 2 givenname: Dan surname: Li fullname: Li, Dan organization: School of Information Engineering (School of Big Data), Xuzhou University of Technology
BookMark	eNp9kE1PwkAQhjcGEwH9A5428Vzdr3bL0aAi0YRE4LwZtgsUyy7uthD49bbURE-eZg7P887k7aGOddYgdEvJPSVEPgRKiWARYSyigg1EdLhAXRpLHknJaOfPfoV6IWwIoUnMRBedJouN0SWeedCfuV3hQ16u8XAN1poCj7yrdvjDrKoCfH6CMncWg83wdOtcwzkbSg-5LQOeh0Yfb3fe7U2Gn44WtrlukL0rqrP5ZnydGnBu8Xg2vUaXSyiCufmZfTR_eZ4NX6P3yWg8fHyPNJOkjBZADGMZTzMpwZCEGU1ZZigkMQeWLnQCWkoaixRACyE46HQQA6-BdEko5X101-bWn31VJpRq4ypv65OKM5FKTkjCa4q1lPYuBG-WaufzLfijokQ1Hau2Y1V3rM4dq0Mt8VYKNWxXxv9G_2N9A-TTgtg
Cites_doi	10.1007/978-3-642-33765-9_50 10.1109/CVPR.2017.515 10.1109/CVPR.2018.00474 10.1109/CVPR.2014.143 10.1007/978-3-030-01216-8_30 10.1155/2022/3887426 10.1561/0100000006 10.5244/C.28.65 10.1109/ACCESS.2020.3038792 10.1109/CVPR42600.2020.00661 10.1007/978-3-319-46454-1_29 10.1109/CVPR.2015.7299094 10.1007/s11276-021-02664-5 10.6036/9844 10.1109/CVPR.2013.312 10.1109/ICCV.2019.00140 10.1016/j.knosys.2020.105526 10.1109/ICCV.2013.343 10.1109/TPAMI.2014.2345390 10.1109/ICCV.2017.129 10.1109/ICCV.2015.490 10.1155/2021/6690237 10.1109/CVPR.2016.90 10.1109/CVPR.2016.156 10.1109/CVPR42600.2020.01104 10.1007/s11263-015-0816-y 10.1109/TPAMI.2014.2388226
ContentType	Journal Article
Copyright	The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.
DBID	AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D
DOI	10.1007/s11042-022-14294-w
DatabaseName	CrossRef Computer and Information Systems 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 Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional
DatabaseTitleList	Computer and Information Systems Abstracts
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	1573-7721
EndPage	35215
ExternalDocumentID	10_1007_s11042_022_14294_w
GroupedDBID	-Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29M 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3EH 4.4 406 408 409 40D 40E 5QI 5VS 67Z 6NX 7WY 8AO 8FE 8FG 8FL 8G5 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBRH ABBXA ABDBE ABDZT ABECU ABFSG ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABRTQ ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACREN ACSNA ACSTC ACZOJ ADHHG ADHIR ADHKG ADIMF ADKFA ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AEZWR AFBBN AFDZB AFEXP AFGCZ AFHIU AFKRA AFLOW AFOHR AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGQPQ AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHPBZ AHSBF AHWEU AHYZX AIAKS AIGIU AIIXL AILAN AITGF AIXLP AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG ATHPR AVWKF AXYYD AYFIA AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ7 GQ8 GUQSH GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITG ITH ITM IWAJR IXC IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW LAK LLZTM M0C M2O M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9O PF0 PHGZM PHGZT PQBIZ PQBZA PQGLB PQQKQ PROAC PT4 PT5 PUEGO Q2X QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TH9 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 ZMTXR ~EX AAYXX AFFHD CITATION 7SC 8FD JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c270t-ba0e22d38d77ae062ec12de1a653a28bc6ac771548aac4443ac895a31a68f0113
IEDL.DBID	RSV
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000900189300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1573-7721 1380-7501
IngestDate	Thu Nov 06 12:22:04 EST 2025 Sat Nov 29 07:32:31 EST 2025 Thu Sep 11 01:10:35 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	29
Keywords	Time series smoothing Sparse representation Correlation filter Object tracking Dynamic convolution kernel
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c270t-ba0e22d38d77ae062ec12de1a653a28bc6ac771548aac4443ac895a31a68f0113
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
PQID	3248730063
PQPubID	54626
PageCount	25
ParticipantIDs	proquest_journals_3248730063 crossref_primary_10_1007_s11042_022_14294_w springer_journals_10_1007_s11042_022_14294_w
PublicationCentury	2000
PublicationDate	20250900
PublicationDateYYYYMMDD	2025-09-01
PublicationDate_xml	– month: 9 year: 2025 text: 20250900
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York – name: Dordrecht
PublicationSubtitle	An International Journal
PublicationTitle	Multimedia tools and applications
PublicationTitleAbbrev	Multimed Tools Appl
PublicationYear	2025
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	14294_CR27 Y Wu (14294_CR35) 2015; 37 14294_CR28 LD Qiu (14294_CR25) 2017; 29 JP Sun (14294_CR30) 2020; 8 D Tian (14294_CR32) 2019; 34 14294_CR23 K Jun (14294_CR19) 2018; 30 14294_CR22 JP Sun (14294_CR31) 2020; 95 JP Sun (14294_CR29) 2021; 2021 O Russakovsky (14294_CR26) 2015; 115 D Yuan (14294_CR37) 2020; 194 SJ Chen (14294_CR4) 2021; 47 L Meng (14294_CR24) 2019; 45 JF Henriques (14294_CR16) 2015; 37 14294_CR15 14294_CR18 D Li (14294_CR21) 2022; 3887426 14294_CR17 14294_CR7 14294_CR6 14294_CR5 14294_CR10 14294_CR34 14294_CR11 14294_CR33 14294_CR9 14294_CR14 14294_CR36 14294_CR8 14294_CR13 RM Gray (14294_CR12) 2006; 2 D Li (14294_CR20) 2021; 27 14294_CR3 14294_CR2 14294_CR1
References_xml	– ident: 14294_CR14 doi: 10.1007/978-3-642-33765-9_50 – ident: 14294_CR22 doi: 10.1109/CVPR.2017.515 – ident: 14294_CR27 doi: 10.1109/CVPR.2018.00474 – ident: 14294_CR5 doi: 10.1109/CVPR.2014.143 – volume: 47 start-page: 630 issue: 3 year: 2021 ident: 14294_CR4 publication-title: Acta Automat Sin – ident: 14294_CR2 doi: 10.1007/978-3-030-01216-8_30 – volume: 3887426 start-page: 12 year: 2022 ident: 14294_CR21 publication-title: J Environ Public Health doi: 10.1155/2022/3887426 – volume: 2 start-page: 155 issue: 3 year: 2006 ident: 14294_CR12 publication-title: Found Trends Commun Inf Theory doi: 10.1561/0100000006 – volume: 30 start-page: 634 issue: 4 year: 2018 ident: 14294_CR19 publication-title: J Comput Aided Des Comput Graph – ident: 14294_CR6 doi: 10.5244/C.28.65 – volume: 8 start-page: 208179 year: 2020 ident: 14294_CR30 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3038792 – ident: 14294_CR33 doi: 10.1109/CVPR42600.2020.00661 – ident: 14294_CR8 doi: 10.1007/978-3-319-46454-1_29 – ident: 14294_CR9 doi: 10.1007/978-3-319-46454-1_29 – ident: 14294_CR10 doi: 10.1109/CVPR.2015.7299094 – volume: 27 start-page: 4389 issue: 7 year: 2021 ident: 14294_CR20 publication-title: Wirel Netw doi: 10.1007/s11276-021-02664-5 – volume: 95 start-page: 646 issue: 6 year: 2020 ident: 14294_CR31 publication-title: DYNA doi: 10.6036/9844 – ident: 14294_CR34 doi: 10.1109/CVPR.2013.312 – ident: 14294_CR18 doi: 10.1109/ICCV.2019.00140 – volume: 45 start-page: 1244 issue: 7 year: 2019 ident: 14294_CR24 publication-title: Acta Automat Sin – volume: 29 start-page: 459 issue: 3 year: 2017 ident: 14294_CR25 publication-title: J Comput Aided Des Comput Graph – volume: 194 year: 2020 ident: 14294_CR37 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2020.105526 – ident: 14294_CR15 doi: 10.1109/ICCV.2013.343 – volume: 37 start-page: 583 issue: 3 year: 2015 ident: 14294_CR16 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2014.2345390 – ident: 14294_CR11 doi: 10.1109/ICCV.2017.129 – ident: 14294_CR7 doi: 10.1109/ICCV.2015.490 – volume: 2021 start-page: 1 year: 2021 ident: 14294_CR29 publication-title: Complexity doi: 10.1155/2021/6690237 – ident: 14294_CR28 – ident: 14294_CR23 doi: 10.1109/CVPR.2017.515 – volume: 34 start-page: 2479 issue: 11 year: 2019 ident: 14294_CR32 publication-title: Control and Decision – ident: 14294_CR13 doi: 10.1109/CVPR.2016.90 – ident: 14294_CR1 doi: 10.1109/CVPR.2016.156 – ident: 14294_CR3 doi: 10.1109/CVPR42600.2020.01104 – volume: 115 start-page: 211 issue: 3 year: 2015 ident: 14294_CR26 publication-title: Int J Comput Vis doi: 10.1007/s11263-015-0816-y – volume: 37 start-page: 1834 issue: 9 year: 2015 ident: 14294_CR35 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2014.2388226 – ident: 14294_CR17 – ident: 14294_CR36
SSID	ssj0016524
Score	2.3977757
Snippet	Aiming at the problem that the correlation between multi-channel feature representation and filter structure is not considered in the objective function...
SourceID	proquest crossref springer
SourceType	Aggregation Database Index Database Publisher
StartPage	35191
SubjectTerms	1231: IoT-driven Computer Vision Technology for Smart Transportation Applications Accuracy Algorithms Artificial neural networks Channels Computer Communication Networks Computer Science Constraints Correlation Data Structures and Information Theory Feature maps Intelligent transportation systems Localization Multimedia Information Systems Neural networks Object recognition Process controls Regularization Spatial data Special Purpose and Application-Based Systems Task complexity Tracking Vision systems
Title	Object Tracking with Channel Group Regularization and Smooth Constraints Using Improved Dynamic Convolution Kernels in ITS
URI	https://link.springer.com/article/10.1007/s11042-022-14294-w https://www.proquest.com/docview/3248730063
Volume	84
WOSCitedRecordID	wos000900189300001&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: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-7721 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016524 issn: 1573-7721 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1RS8MwED50-qAPTqfidEoefNPCmqRt-ijqUIQp25S9lTRNQNBO2rqBv94kba2KPuhzryEkl7vL5b77AI5DrLxQEeUoTIVDla8cHirpMB0qqzjwqUuVJZsIhkM2nYZ3FSgsr6vd6ydJa6kbsJtroCSm-tzVRpQ6i2VY0e6OGcKG0fjh4-3A9zCt4DE___fVBTVx5benUOthBu3_zW0TNqqIEp2VKrAFSzLtQLtma0DV4e3A-qfWg9vwdhubDAzSvkqYbDkyCVlksAapfEI2I4VGlqc-q5CaiKcJGj_PZkbORJWGXKLIkS06QGVyQibooqS4NyLzSq3Rjcz0qDl6TNH1ZLwD94PLyfmVU_EwOAIH_cKJeV9inBCWBAGXfR9L4eJEutz3CMcsFj4XQWDuPpwLSinhgoUeJ1qAKW0_yC600lkq9wAlhqQ0xh7Rt0rtPhkTHqeUC9_jTF_c-l04qbcmeinbbURNY2WzyJFe5MgucrToQq_evag6enmkI0RmmvD7pAun9W41n38fbf9v4gewhg0XsK0360GryF7lIayKefGYZ0dWJd8BX0XeuQ
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS8MwED90CuqD06k4nZoH37SwpmmbPoo6HJtTtil7K2mawEA7aecG_vUmWWtV9EGfew3hcrmv3N0P4DTA0g2kIy2JCbeI9KTFAiksqlxlGfkesYk0YBN-r0dHo-A-bwrLimr34knSaOqy2c3WrSS6-txWSpRY82VYIcpi6Yn5_cHjx9uB52KSt8f8_N9XE1T6ld-eQo2FaVX_t7ct2Mw9SnSxEIFtWBJJDaoFWgPKL28NNj6NHtyBt7tIZ2CQslVcZ8uRTsgi3WuQiCdkMlKob3Dq07xTE7EkRoPnyUTTaa9Sg0tMM2SKDtAiOSFidLWAuNcks1ysUUekatUMjRPUHg524aF1Pby8sXIcBotjvzm1ItYUGMcOjX2fiaaHBbdxLGzmuQ7DNOIe476vYx_GOCHEYZwGLnMUAZVKfzh7UEkmidgHFGuQ0gi7jooqlfmklLuMEMY9l1EVuDXrcFYcTfiyGLcRloOVNZNDxeTQMDmc16FRnF6YX70sVB4i1UP4PacO58VplZ9_X-3gb-QnsHYzvO2G3XavcwjrWOMCm9qzBlSm6as4glU-m46z9NiI5zvYpuGd
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NS8MwFH_oFNGD06k4P3PwpsU1Tdv0KM6hKFPcFG8lTRMYuE626tC_3rysdSp6EM99DSF5yfvI-70fwEFEtR9pTzuaMukwHWhHRFo53LjKOgkD5jJtySbCdps_PEQ3n1D8ttq9fJKcYBqwS1OWHz-l-ngKfHMRVoKV6K65UJkznoU5hoX0GK937j_eEQKfsgIq8_N_X83R1Mf89ixqrU2r-v95rsBy4WmSk4lqrMKMympQLVkcSHGoa7D0qSXhGrxdJ5iZIcaGScyiE0zUEsQgZOqR2EwVubX89cMCwUlElpJOfzBAOfQ2kXQiHxFbjEAmSQuVkuZrJvo9iSIvhbqTSzU0o45ILyMX3c463LXOuqfnTsHP4EgaNnInEQ1FaerxNAyFagRUSZemyhWB7wnKExkIGYYYEwkhGWOekDzyhWcEuDb3ircBlWyQqU0gKZKXJtT3TLRpzCrn0heMCRn4gpuArlGHw3Kb4qdJG4542nAZFzk2ixzbRY7HddgpdzIujuQoNp4jx-b8gVeHo3Lnpp9_H23rb-L7sHDTbMVXF-3LbVikSBdsS9J2oJIPn9UuzMuXvDca7llNfQcl4uqB
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=Object+Tracking+with+Channel+Group+Regularization+and+Smooth+Constraints+Using+Improved+Dynamic+Convolution+Kernels+in+ITS&rft.jtitle=Multimedia+tools+and+applications&rft.au=Sun%2C+Jinping&rft.au=Li%2C+Dan&rft.date=2025-09-01&rft.pub=Springer+US&rft.eissn=1573-7721&rft.volume=84&rft.issue=29&rft.spage=35191&rft.epage=35215&rft_id=info:doi/10.1007%2Fs11042-022-14294-w&rft.externalDocID=10_1007_s11042_022_14294_w
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1573-7721&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1573-7721&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1573-7721&client=summon