Multisource surveillance video data coding with hierarchical knowledge library

The rapidly increasing surveillance video data has challenged the existing video coding standards. Even though knowledge based video coding scheme has been proposed to remove redundancy of moving objects across multiple videos and achieved great coding efficiency improvement, it still has difficulti...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Multimedia tools and applications Ročník 78; číslo 11; s. 14705 - 14731
Hlavní autoři:	Chen, Yu, Hu, Ruimin, Xiao, Jing, Xu, Liang, Wang, Zhongyuan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.06.2019 Springer Nature B.V
Témata:	Coding Coding standards Computer Communication Networks Computer Science Data Structures and Information Theory Knowledge Multimedia Information Systems Redundancy Special Purpose and Application-Based Systems Surveillance Video data Redundancy removal Hybrid prediction Surveillance video data Visual changes Hierarchical knowledge extraction
ISSN:	1380-7501, 1573-7721
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	The rapidly increasing surveillance video data has challenged the existing video coding standards. Even though knowledge based video coding scheme has been proposed to remove redundancy of moving objects across multiple videos and achieved great coding efficiency improvement, it still has difficulties to cope with complicated visual changes of objects resulting from various factors. In this paper, a novel hierarchical knowledge extraction method is proposed. Common knowledge on three coarse-to-fine levels, namely category level, object level and video level, are extracted from history data to model the initial appearance, stable changes and temporal changes respectively for better object representation and redundancy removal. In addition, we apply the extracted hierarchical knowledge to surveillance video coding tasks and establish a hybrid prediction based coding framework. On the one hand, hierarchical knowledge is projected to the image plane to generate reference for I frames to achieve better prediction performance. On the other hand, we develop a transform based prediction for P/B frames to reduce the computational complexity while improve the coding efficiency. Experimental results demonstrate the effectiveness of our proposed method.
AbstractList	The rapidly increasing surveillance video data has challenged the existing video coding standards. Even though knowledge based video coding scheme has been proposed to remove redundancy of moving objects across multiple videos and achieved great coding efficiency improvement, it still has difficulties to cope with complicated visual changes of objects resulting from various factors. In this paper, a novel hierarchical knowledge extraction method is proposed. Common knowledge on three coarse-to-fine levels, namely category level, object level and video level, are extracted from history data to model the initial appearance, stable changes and temporal changes respectively for better object representation and redundancy removal. In addition, we apply the extracted hierarchical knowledge to surveillance video coding tasks and establish a hybrid prediction based coding framework. On the one hand, hierarchical knowledge is projected to the image plane to generate reference for I frames to achieve better prediction performance. On the other hand, we develop a transform based prediction for P/B frames to reduce the computational complexity while improve the coding efficiency. Experimental results demonstrate the effectiveness of our proposed method.
Author	Xu, Liang Xiao, Jing Hu, Ruimin Chen, Yu Wang, Zhongyuan
Author_xml	– sequence: 1 givenname: Yu surname: Chen fullname: Chen, Yu organization: National Engineering Research Center for Multimedia and Software, Wuhan University – sequence: 2 givenname: Ruimin surname: Hu fullname: Hu, Ruimin email: hrm@whu.edu.cn organization: National Engineering Research Center for Multimedia and Software, Wuhan University, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University – sequence: 3 givenname: Jing surname: Xiao fullname: Xiao, Jing organization: National Engineering Research Center for Multimedia and Software, Wuhan University – sequence: 4 givenname: Liang surname: Xu fullname: Xu, Liang organization: National Engineering Research Center for Multimedia and Software, Wuhan University – sequence: 5 givenname: Zhongyuan surname: Wang fullname: Wang, Zhongyuan organization: National Engineering Research Center for Multimedia and Software, Wuhan University
BookMark	eNp9kMtKAzEUhoNUsK0-gLsB19FcJsnMUoo3qLrRdcgkmTZ1zNQk0-LbmzKCIOjqXDjf-c_5Z2Die28BOMfoEiMkriLGqCQQ4QryijBYHoEpZoJCIQie5JxWCAqG8AmYxbhBCHNGyil4ehy65GI_BG2LOISddV2nfC52zti-MCqpQvfG-VWxd2ldrJ0NKui106or3ny_76xZ2aJzTW5_noLjVnXRnn3HOXi9vXlZ3MPl893D4noJNcU8wYboFlFLCNPClJVtBK8N16pCpsQVa0xuM8sF40IJZmtOlWlrRamtia5ynIOLce829B-DjUlu8gs-S0qCKWGEUX6YwuOUDn2MwbZyG9x7PlNiJA-2ydE2mW2TB9tkmRnxi9EuqeR6n4Jy3b8kGcmYVfzKhp-b_oa-AAcQhEo
CitedBy_id	crossref_primary_10_1007_s11280_020_00793_z crossref_primary_10_3390_rs12030497
Cites_doi	10.1109/CVPR.2011.5995616 10.1145/2601097.2601206 10.1109/TPAMI.2004.1262177 10.1016/j.neucom.2015.08.096 10.1109/TMM.2017.2766043 10.1109/TMM.2016.2645398 10.1109/ICIP.2017.8296285 10.1109/TCSVT.2010.2041820 10.1109/TIP.2016.2551366 10.1023/A:1007924428535 10.1109/JPROC.2010.2098830 10.1109/ISM.2016.0065 10.1109/TIP.2014.2352036 10.1109/TCSVT.2015.2389511 10.1007/978-3-319-48890-5_28 10.1109/TBC.2017.2781118 10.1109/TCSVT.2012.2221191 10.1109/TMM.2016.2581590 10.1109/TIP.2018.2867740 10.1145/2502081.2502145 10.1609/aaai.v30i1.9969 10.1109/TMM.2011.2180705 10.1609/aaai.v30i1.10155 10.1109/TMM.2013.2239629 10.1007/978-3-642-33718-5_60 10.1109/TPAMI.2003.1195991 10.1109/TCSVT.2003.815165 10.1007/978-3-319-10602-1_54 10.1145/2393347.2396294 10.1145/1101149.1101289 10.1109/TCSVT.2015.2477935 10.1109/ICME.2013.6607570 10.1109/ICALIP.2012.6376591 10.1109/TMM.2018.2867742 10.1109/TBDATA.2017.2715815
ContentType	Journal Article
Copyright	Springer Science+Business Media, LLC, part of Springer Nature 2018 Multimedia Tools and Applications is a copyright of Springer, (2018). All Rights Reserved.
Copyright_xml	– notice: Springer Science+Business Media, LLC, part of Springer Nature 2018 – notice: Multimedia Tools and Applications is a copyright of Springer, (2018). All Rights Reserved.
DBID	AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8AO 8FD 8FE 8FG 8FK 8FL 8G5 ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ GUQSH HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N M2O MBDVC P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI Q9U
DOI	10.1007/s11042-018-6825-4
DatabaseName	CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni Edition) Research Library (Alumni Edition) ProQuest Central (Alumni) ProQuest Central UK/Ireland Health Research Premium Collection ProQuest Central Essentials ProQuest Central Business Premium Collection Technology collection ProQuest One Community College ProQuest Central Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student Research Library Prep SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Research Library Research Library (Corporate) ProQuest advanced technologies & aerospace journals ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Business (OCUL) ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central Basic
DatabaseTitle	CrossRef ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Research Library Prep Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Research Library (Alumni Edition) ProQuest Pharma Collection ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Research Library ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) Business Premium Collection (Alumni)
DatabaseTitleList	ABI/INFORM Global (Corporate)
Database_xml	– sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	1573-7721
EndPage	14731
ExternalDocumentID	10_1007_s11042_018_6825_4
GroupedDBID	-4Z -59 -5G -BR -EM -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 203 29M 2J2 2JN 2JY 2KG 2LR 2~H 30V 4.4 406 408 409 40D 40E 5VS 67Z 6NX 7WY 8AO 8FE 8FG 8FL 8G5 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABUWG ABWNU ABXPI ACAOD ACDTI ACGFO ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACREN ACSNA ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEFQL AEGAL AEGNC AEJHL AEJRE AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFKRA AFLOW AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CCPQU 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 GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GUQSH GXS 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 LAK LLZTM M0C M0N M2O M4Y MA- N9A NB0 NPVJJ NQJWS NU0 O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PT5 Q2X QOK QOS R89 R9I RHV RNS ROL RPX RSV S16 S27 S3B SAP SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TH9 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX VC2 W23 W48 WK8 YLTOR Z45 Z7R Z7S Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~EX -Y2 1SB 2.D 28- 2P1 2VQ 3EH 5QI AAOBN AAPKM AARHV AAYTO AAYXX ABBRH ABDBE ABFSG ABQSL ABRTQ ABULA ACBXY ACSTC ADHKG ADKFA AEBTG AEFIE AEKMD AEZWR AFDZB AFEXP AFFHD AFGCZ AFHIU AFOHR AGGDS AGJBK AGQPQ AHPBZ AHWEU AIXLP AJBLW ATHPR AYFIA BBWZM CAG CITATION COF H13 KOW N2Q NDZJH O9- OVD PHGZM PHGZT PQGLB R4E RNI RZC RZE RZK S1Z S26 S28 SCJ SCLPG T16 TEORI UZXMN VFIZW 3V. 7SC 7XB 8AL 8FD 8FK JQ2 L.- L7M L~C L~D MBDVC PKEHL PQEST PQUKI Q9U
ID	FETCH-LOGICAL-c316t-b2cf03e225c7d48eb769d6ca80d4185bdc7d5e67567a75e963adf9a33e92c8a33
IEDL.DBID	RSV
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000474249700026&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1380-7501
IngestDate	Tue Nov 04 22:08:15 EST 2025 Sat Nov 29 03:26:04 EST 2025 Tue Nov 18 22:25:29 EST 2025 Fri Feb 21 02:37:30 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	11
Keywords	Redundancy removal Hybrid prediction Surveillance video data Visual changes Hierarchical knowledge extraction
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c316t-b2cf03e225c7d48eb769d6ca80d4185bdc7d5e67567a75e963adf9a33e92c8a33
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
PQID	2132525363
PQPubID	54626
PageCount	27
ParticipantIDs	proquest_journals_2132525363 crossref_primary_10_1007_s11042_018_6825_4 crossref_citationtrail_10_1007_s11042_018_6825_4 springer_journals_10_1007_s11042_018_6825_4
PublicationCentury	2000
PublicationDate	2019-06-01
PublicationDateYYYYMMDD	2019-06-01
PublicationDate_xml	– month: 06 year: 2019 text: 2019-06-01 day: 01
PublicationDecade	2010
PublicationPlace	New York
PublicationPlace_xml	– name: New York – name: Dordrecht
PublicationSubtitle	An International Journal
PublicationTitle	Multimedia tools and applications
PublicationTitleAbbrev	Multimed Tools Appl
PublicationYear	2019
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	Azizpour, H., & Laptev, I. (2012). Object detection using strongly-supervised deformable part models. In European Conference on Computer Vision, Springer, pp. 836-849 Guo, X., Li, S., & Cao, X. (2013). Motion matters: A novel framework for compressing surveillance videos. In Proceedings of the 21st ACM international conference on Multimedia, ACM, pp. 549-552 XiaoJHuRLiaoLChenYWangZXiongZKnowledge-based coding of objects for multisource surveillance video dataIEEE Trans Multimedia20161891691170610.1109/TMM.2016.2581590 Weinzaepfel, P., Jégou, H., & Pérez, P. (2011). Reconstructing an image from its local descriptors. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE, pp. 337-344 WangHTianTMaMWuJJoint Compression of Near-Duplicate VideosIEEE Trans Multimedia201719590892010.1109/TMM.2016.2645398 ZhangXTianYHuangTDongSGaoWOptimizing the hierarchical prediction and coding in HEVC for surveillance and conference videos with background modelingIEEE Trans Image Process2014231045114526330009810.1109/TIP.2014.23520361374.94461 Chen, C., Cai, J., Lin, W., & Shi, G. (2012). Surveillance video coding via low-rank and sparse decomposition. In Proceedings of the 20th ACM international conference on Multimedia, ACM, pp. 713-716 ShaoZCaiJWangZSmart Monitoring Cameras Driven Intelligent Processing to Big Surveillance Video DataIEEE Transactions on Big Data20184110511610.1109/TBDATA.2017.2715815 TsaiTHLinCYExploring contextual redundancy in improving object-based video coding for video sensor networks surveillanceIEEE Trans Multimedia201214366968210.1109/TMM.2011.2180705 VetroAWiegandTSullivanGJOverview of the stereo and multiview video coding extensions of the H. 264/MPEG-4 AVC standardProc IEEE201199462664210.1109/JPROC.2010.2098830 Ma, C., Liu, D., Peng, X., & Wu, F. (2017). Surveillance video coding with vehicle library. In Image Processing (ICIP), 2017 IEEE International Conference on, IEEE, pp. 270-274 Shi, Z., Sun, X., & Wu, F. (2013). Feature-based image set compression. In Multimedia and Expo (ICME), 2013 IEEE International Conference on, IEEE, pp. 1-6 YueHSunXYangJWuFCloud-based image coding for mobile devices—Toward thousands to one compressionIEEE Trans Multimedia201315484585710.1109/TMM.2013.2239629 Hakeem, A., Shafique, K., & Shah, M. (2005). An object-based video coding framework for video sequences obtained from static cameras. In Proceedings of the 13th annual ACM international conference on Multimedia, ACM, pp. 608-617 LinCZhaoYXiaoJTilloTRegion-based multiple description coding for multiview video plus depth videoIEEE Trans Multimedia20182051209122310.1109/TMM.2017.2766043 Au, O., Li, S., Zou, R., Dai, W., & Sun, L. (2012). Digital photo album compression based on global motion compensation and intra/inter prediction. In Audio, Language and Image Processing (ICALIP), 2012 International Conference on, IEEE, pp. 84-90 BellSBalaKSnavelyNIntrinsic images in the wildACM Trans Graph201433415910.1145/2601097.2601206 PaulMEfficient Multiview Video Coding Using 3-D Coding and Saliency-Based Bit AllocationIEEE Trans Broadcast201864223524610.1109/TBC.2017.2781118 WuHSunXYangJZengWWuFLossless compression of JPEG coded photo collectionsIEEE Trans Image Process201625626842696349365810.1109/TIP.2016.255136607011700 Liu, Y., Nie, L., Han, L., Zhang, L., & Rosenblum, D. S. (2015). Action2Activity: Recognizing Complex Activities from Sensor Data. In IJCAI, pp. 1617-1623 Liu, L., Cheng, L., Liu, Y., Jia, Y., & Rosenblum, D. S. (2016). Recognizing Complex Activities by a Probabilistic Interval-Based Model. In AAAI, pp. 1266-1272 YangYLiuQHeXLiuZCross-View Multi-Lateral Filter for Compressed Multi-View Depth VideoIEEE Trans Image Process2019281302315386318310.1109/TIP.2018.286774007014387 Waechter, M., Moehrle, N., & Goesele, M. (2014). Let there be color! Large-scale texturing of 3D reconstructions. In European Conference on Computer Vision, Springer, pp. 836-850 WiegandTSullivanGJBjontegaardGLuthraAOverview of the H. 264/AVC video coding standardIEEE Trans circuits syst video technol200313756057610.1109/TCSVT.2003.815165 NgKTWuQChanSCShumHYObject-based coding for plenoptic videosIEEE Trans Circuits Syst Video Technol201020454856210.1109/TCSVT.2010.2041820 LiuYNieLLiuLRosenblumDSFrom action to activity: sensor-based activity recognitionNeurocomputing201618110811510.1016/j.neucom.2015.08.096 Bjontegarrd, G. (2001). Calculation of average PSNR differences between RD-curves. VCEG-M33 HM 16.20. https://hevc.hhi.fraunhofer.de. Accessed 14 Sept 2018 ComaniciuDRameshVMeerPKernel-based object trackingIEEE Trans Pattern Anal Mach Intell200325556457710.1109/TPAMI.2003.1195991 Liu, Y., Zhang, L., Nie, L., Yan, Y., & Rosenblum, D. S. (2016). Fortune Teller: Predicting Your Career Path. In AAAI, pp. 201-207 TechGChenYMüllerKOhmJRVetroAWangYKOverview of the multiview and 3D extensions of high efficiency video codingIEEE Trans Circuits Syst Video Technol2016261354910.1109/TCSVT.2015.2477935 PuricaAIMoraEGPesquet-PopescuBCagnazzoMIonescuBMultiview plus depth video coding with temporal prediction view synthesisIEEE Trans Circuits Syst Video Technol201626236037410.1109/TCSVT.2015.2389511 KolmogorovVZabinRWhat energy functions can be minimized via graph cutsIEEE Trans Pattern Anal Mach Intell200426214715910.1109/TPAMI.2004.1262177 TanTNSullivanGDBakerKDModel-based localisation and recognition of road vehiclesInt J Comput Vis199827152510.1023/A:1007924428535 Sreedhar, K. K., Aminlou, A., Hannuksela, M. M., & Gabbouj, M. (2016). Standard-compliant multiview video coding and streaming for virtual reality applications. In Multimedia (ISM), 2016 IEEE International Symposium on, IEEE, pp. 295-300 SullivanGJOhmJHanWJWiegandTOverview of the high efficiency video coding (HEVC) standardIEEE Trans circuits syst video technol201222121649166810.1109/TCSVT.2012.2221191 Wang, Q., Wang, Z., Xiao, J., Xiao, J., & Li, W. (2016). Fine-Grained Vehicle Recognition in Traffic Surveillance. In Pacific Rim Conference on Multimedia, Springer, pp. 285-295 Yang, Y., Li, B., Li, P., & Liu, Q. (2018). A Two-Stage Clustering Based 3D Visual Saliency Model for Dynamic Scenarios. IEEE Transactions on Multimedia TH Tsai (6825_CR26) 2012; 14 T Wiegand (6825_CR32) 2003; 13 H Wu (6825_CR33) 2016; 25 J Xiao (6825_CR34) 2016; 18 X Zhang (6825_CR38) 2014; 23 6825_CR29 AI Purica (6825_CR19) 2016; 26 H Yue (6825_CR37) 2013; 15 6825_CR28 6825_CR21 6825_CR22 C Lin (6825_CR11) 2018; 20 V Kolmogorov (6825_CR10) 2004; 26 GJ Sullivan (6825_CR23) 2012; 22 Y Yang (6825_CR36) 2019; 28 KT Ng (6825_CR17) 2010; 20 S Bell (6825_CR3) 2014; 33 A Vetro (6825_CR27) 2011; 99 6825_CR5 6825_CR8 TN Tan (6825_CR24) 1998; 27 6825_CR7 6825_CR2 6825_CR1 D Comaniciu (6825_CR6) 2003; 25 6825_CR4 H Wang (6825_CR30) 2017; 19 Y Liu (6825_CR13) 2016; 181 6825_CR31 6825_CR9 6825_CR14 6825_CR15 6825_CR16 M Paul (6825_CR18) 2018; 64 Z Shao (6825_CR20) 2018; 4 G Tech (6825_CR25) 2016; 26 6825_CR12 6825_CR35
References_xml	– reference: SullivanGJOhmJHanWJWiegandTOverview of the high efficiency video coding (HEVC) standardIEEE Trans circuits syst video technol201222121649166810.1109/TCSVT.2012.2221191 – reference: PuricaAIMoraEGPesquet-PopescuBCagnazzoMIonescuBMultiview plus depth video coding with temporal prediction view synthesisIEEE Trans Circuits Syst Video Technol201626236037410.1109/TCSVT.2015.2389511 – reference: KolmogorovVZabinRWhat energy functions can be minimized via graph cutsIEEE Trans Pattern Anal Mach Intell200426214715910.1109/TPAMI.2004.1262177 – reference: Chen, C., Cai, J., Lin, W., & Shi, G. (2012). Surveillance video coding via low-rank and sparse decomposition. In Proceedings of the 20th ACM international conference on Multimedia, ACM, pp. 713-716 – reference: NgKTWuQChanSCShumHYObject-based coding for plenoptic videosIEEE Trans Circuits Syst Video Technol201020454856210.1109/TCSVT.2010.2041820 – reference: Bjontegarrd, G. (2001). Calculation of average PSNR differences between RD-curves. VCEG-M33 – reference: Guo, X., Li, S., & Cao, X. (2013). Motion matters: A novel framework for compressing surveillance videos. In Proceedings of the 21st ACM international conference on Multimedia, ACM, pp. 549-552 – reference: Ma, C., Liu, D., Peng, X., & Wu, F. (2017). Surveillance video coding with vehicle library. In Image Processing (ICIP), 2017 IEEE International Conference on, IEEE, pp. 270-274 – reference: YueHSunXYangJWuFCloud-based image coding for mobile devices—Toward thousands to one compressionIEEE Trans Multimedia201315484585710.1109/TMM.2013.2239629 – reference: Liu, Y., Nie, L., Han, L., Zhang, L., & Rosenblum, D. S. (2015). Action2Activity: Recognizing Complex Activities from Sensor Data. In IJCAI, pp. 1617-1623 – reference: Hakeem, A., Shafique, K., & Shah, M. (2005). An object-based video coding framework for video sequences obtained from static cameras. In Proceedings of the 13th annual ACM international conference on Multimedia, ACM, pp. 608-617 – reference: YangYLiuQHeXLiuZCross-View Multi-Lateral Filter for Compressed Multi-View Depth VideoIEEE Trans Image Process2019281302315386318310.1109/TIP.2018.286774007014387 – reference: Weinzaepfel, P., Jégou, H., & Pérez, P. (2011). Reconstructing an image from its local descriptors. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE, pp. 337-344 – reference: PaulMEfficient Multiview Video Coding Using 3-D Coding and Saliency-Based Bit AllocationIEEE Trans Broadcast201864223524610.1109/TBC.2017.2781118 – reference: VetroAWiegandTSullivanGJOverview of the stereo and multiview video coding extensions of the H. 264/MPEG-4 AVC standardProc IEEE201199462664210.1109/JPROC.2010.2098830 – reference: Liu, L., Cheng, L., Liu, Y., Jia, Y., & Rosenblum, D. S. (2016). Recognizing Complex Activities by a Probabilistic Interval-Based Model. In AAAI, pp. 1266-1272 – reference: WiegandTSullivanGJBjontegaardGLuthraAOverview of the H. 264/AVC video coding standardIEEE Trans circuits syst video technol200313756057610.1109/TCSVT.2003.815165 – reference: HM 16.20. https://hevc.hhi.fraunhofer.de. Accessed 14 Sept 2018 – reference: Wang, Q., Wang, Z., Xiao, J., Xiao, J., & Li, W. (2016). Fine-Grained Vehicle Recognition in Traffic Surveillance. In Pacific Rim Conference on Multimedia, Springer, pp. 285-295 – reference: LiuYNieLLiuLRosenblumDSFrom action to activity: sensor-based activity recognitionNeurocomputing201618110811510.1016/j.neucom.2015.08.096 – reference: XiaoJHuRLiaoLChenYWangZXiongZKnowledge-based coding of objects for multisource surveillance video dataIEEE Trans Multimedia20161891691170610.1109/TMM.2016.2581590 – reference: WangHTianTMaMWuJJoint Compression of Near-Duplicate VideosIEEE Trans Multimedia201719590892010.1109/TMM.2016.2645398 – reference: WuHSunXYangJZengWWuFLossless compression of JPEG coded photo collectionsIEEE Trans Image Process201625626842696349365810.1109/TIP.2016.255136607011700 – reference: TanTNSullivanGDBakerKDModel-based localisation and recognition of road vehiclesInt J Comput Vis199827152510.1023/A:1007924428535 – reference: Waechter, M., Moehrle, N., & Goesele, M. (2014). Let there be color! Large-scale texturing of 3D reconstructions. In European Conference on Computer Vision, Springer, pp. 836-850 – reference: Au, O., Li, S., Zou, R., Dai, W., & Sun, L. (2012). Digital photo album compression based on global motion compensation and intra/inter prediction. In Audio, Language and Image Processing (ICALIP), 2012 International Conference on, IEEE, pp. 84-90 – reference: ComaniciuDRameshVMeerPKernel-based object trackingIEEE Trans Pattern Anal Mach Intell200325556457710.1109/TPAMI.2003.1195991 – reference: LinCZhaoYXiaoJTilloTRegion-based multiple description coding for multiview video plus depth videoIEEE Trans Multimedia20182051209122310.1109/TMM.2017.2766043 – reference: TechGChenYMüllerKOhmJRVetroAWangYKOverview of the multiview and 3D extensions of high efficiency video codingIEEE Trans Circuits Syst Video Technol2016261354910.1109/TCSVT.2015.2477935 – reference: TsaiTHLinCYExploring contextual redundancy in improving object-based video coding for video sensor networks surveillanceIEEE Trans Multimedia201214366968210.1109/TMM.2011.2180705 – reference: ZhangXTianYHuangTDongSGaoWOptimizing the hierarchical prediction and coding in HEVC for surveillance and conference videos with background modelingIEEE Trans Image Process2014231045114526330009810.1109/TIP.2014.23520361374.94461 – reference: Liu, Y., Zhang, L., Nie, L., Yan, Y., & Rosenblum, D. S. (2016). Fortune Teller: Predicting Your Career Path. In AAAI, pp. 201-207 – reference: Shi, Z., Sun, X., & Wu, F. (2013). Feature-based image set compression. In Multimedia and Expo (ICME), 2013 IEEE International Conference on, IEEE, pp. 1-6 – reference: Sreedhar, K. K., Aminlou, A., Hannuksela, M. M., & Gabbouj, M. (2016). Standard-compliant multiview video coding and streaming for virtual reality applications. In Multimedia (ISM), 2016 IEEE International Symposium on, IEEE, pp. 295-300 – reference: Azizpour, H., & Laptev, I. (2012). Object detection using strongly-supervised deformable part models. In European Conference on Computer Vision, Springer, pp. 836-849 – reference: BellSBalaKSnavelyNIntrinsic images in the wildACM Trans Graph201433415910.1145/2601097.2601206 – reference: Yang, Y., Li, B., Li, P., & Liu, Q. (2018). A Two-Stage Clustering Based 3D Visual Saliency Model for Dynamic Scenarios. IEEE Transactions on Multimedia – reference: ShaoZCaiJWangZSmart Monitoring Cameras Driven Intelligent Processing to Big Surveillance Video DataIEEE Transactions on Big Data20184110511610.1109/TBDATA.2017.2715815 – ident: 6825_CR31 doi: 10.1109/CVPR.2011.5995616 – volume: 33 start-page: 159 issue: 4 year: 2014 ident: 6825_CR3 publication-title: ACM Trans Graph doi: 10.1145/2601097.2601206 – volume: 26 start-page: 147 issue: 2 year: 2004 ident: 6825_CR10 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2004.1262177 – ident: 6825_CR9 – volume: 181 start-page: 108 year: 2016 ident: 6825_CR13 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.08.096 – volume: 20 start-page: 1209 issue: 5 year: 2018 ident: 6825_CR11 publication-title: IEEE Trans Multimedia doi: 10.1109/TMM.2017.2766043 – volume: 19 start-page: 908 issue: 5 year: 2017 ident: 6825_CR30 publication-title: IEEE Trans Multimedia doi: 10.1109/TMM.2016.2645398 – ident: 6825_CR16 doi: 10.1109/ICIP.2017.8296285 – volume: 20 start-page: 548 issue: 4 year: 2010 ident: 6825_CR17 publication-title: IEEE Trans Circuits Syst Video Technol doi: 10.1109/TCSVT.2010.2041820 – volume: 25 start-page: 2684 issue: 6 year: 2016 ident: 6825_CR33 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2016.2551366 – ident: 6825_CR4 – volume: 27 start-page: 5 issue: 1 year: 1998 ident: 6825_CR24 publication-title: Int J Comput Vis doi: 10.1023/A:1007924428535 – volume: 99 start-page: 626 issue: 4 year: 2011 ident: 6825_CR27 publication-title: Proc IEEE doi: 10.1109/JPROC.2010.2098830 – ident: 6825_CR22 doi: 10.1109/ISM.2016.0065 – volume: 23 start-page: 4511 issue: 10 year: 2014 ident: 6825_CR38 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2014.2352036 – volume: 26 start-page: 360 issue: 2 year: 2016 ident: 6825_CR19 publication-title: IEEE Trans Circuits Syst Video Technol doi: 10.1109/TCSVT.2015.2389511 – ident: 6825_CR29 doi: 10.1007/978-3-319-48890-5_28 – volume: 64 start-page: 235 issue: 2 year: 2018 ident: 6825_CR18 publication-title: IEEE Trans Broadcast doi: 10.1109/TBC.2017.2781118 – volume: 22 start-page: 1649 issue: 12 year: 2012 ident: 6825_CR23 publication-title: IEEE Trans circuits syst video technol doi: 10.1109/TCSVT.2012.2221191 – volume: 18 start-page: 1691 issue: 9 year: 2016 ident: 6825_CR34 publication-title: IEEE Trans Multimedia doi: 10.1109/TMM.2016.2581590 – volume: 28 start-page: 302 issue: 1 year: 2019 ident: 6825_CR36 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2018.2867740 – ident: 6825_CR7 doi: 10.1145/2502081.2502145 – ident: 6825_CR15 doi: 10.1609/aaai.v30i1.9969 – volume: 14 start-page: 669 issue: 3 year: 2012 ident: 6825_CR26 publication-title: IEEE Trans Multimedia doi: 10.1109/TMM.2011.2180705 – ident: 6825_CR14 doi: 10.1609/aaai.v30i1.10155 – volume: 15 start-page: 845 issue: 4 year: 2013 ident: 6825_CR37 publication-title: IEEE Trans Multimedia doi: 10.1109/TMM.2013.2239629 – ident: 6825_CR2 doi: 10.1007/978-3-642-33718-5_60 – volume: 25 start-page: 564 issue: 5 year: 2003 ident: 6825_CR6 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2003.1195991 – volume: 13 start-page: 560 issue: 7 year: 2003 ident: 6825_CR32 publication-title: IEEE Trans circuits syst video technol doi: 10.1109/TCSVT.2003.815165 – ident: 6825_CR28 doi: 10.1007/978-3-319-10602-1_54 – ident: 6825_CR5 doi: 10.1145/2393347.2396294 – ident: 6825_CR8 doi: 10.1145/1101149.1101289 – volume: 26 start-page: 35 issue: 1 year: 2016 ident: 6825_CR25 publication-title: IEEE Trans Circuits Syst Video Technol doi: 10.1109/TCSVT.2015.2477935 – ident: 6825_CR21 doi: 10.1109/ICME.2013.6607570 – ident: 6825_CR1 doi: 10.1109/ICALIP.2012.6376591 – ident: 6825_CR12 – ident: 6825_CR35 doi: 10.1109/TMM.2018.2867742 – volume: 4 start-page: 105 issue: 1 year: 2018 ident: 6825_CR20 publication-title: IEEE Transactions on Big Data doi: 10.1109/TBDATA.2017.2715815
SSID	ssj0016524
Score	2.179484
Snippet	The rapidly increasing surveillance video data has challenged the existing video coding standards. Even though knowledge based video coding scheme has been...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	14705
SubjectTerms	Coding Coding standards Computer Communication Networks Computer Science Data Structures and Information Theory Knowledge Multimedia Information Systems Redundancy Special Purpose and Application-Based Systems Surveillance Video data
SummonAdditionalLinks	– databaseName: ABI/INFORM Global dbid: M0C link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07T8MwED5BYYCBQgFRKMgDE8jCTuzEnhCqqBigYgCpW2Q7zoTa0tfvx06cBpDowhQpcawon893vtcHcC2ILaiyDKeEKsxSU2AttcSaM5PIJFVUl931n9PhUIxG8jU43OYhrbLeE8uNOp8Y7yO_i9yxiUc8TuL76Sf2rFE-uhooNLZhx1s2PqXvhfTXUYSEB1JbQbDTjLSOapalc9QXphAqcOIOSZj91EuNsfkrPlqqnUH7vx98CAfB4EQP1Qo5gi077kC7JnNAQbY7sP-tM-ExDMvC3Mqzj-bL2cp6diI3EPm6vQnyiaXITLziQ96VizyldhmUcJijtaMOBSfRCbwPHt_6TzhQL2AT02SBdWQKElsn7CbNmbA6TWSeGCVI7rvd6Nzd5tYdNhyaKbdOilVeSBXHVkZGuOsptMaTsT0DlMeUKxVFmmnKrGAqssKZiZHkeaGJol0g9Y_PTOhL7ukxPrKmo7LHKnNYZR6rjHXhZv3KtGrKsWlwr8YnC_I5zxpwunBbI9w8_nOy882TXcCeM6hklUrWg9ZitrSXsGtWDrHZVbk4vwDj6-oX priority: 102 providerName: ProQuest
Title	Multisource surveillance video data coding with hierarchical knowledge library
URI	https://link.springer.com/article/10.1007/s11042-018-6825-4 https://www.proquest.com/docview/2132525363
Volume	78
WOSCitedRecordID	wos000474249700026&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 customDbUrl: eissn: 1573-7721 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016524 issn: 1380-7501 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/eLvHCXMwnV3dT9swED_x9QAPlPGhdUDlhz2BLNmOHduPowIhsXUVYxvjJbId5wm1Uwv8_ZzTpAW0IcGLoySOZd35cuc73_0APhsWK-6ipJpxR6UOFfXWW-qVDLnNteO-rq7_VQ8G5vraDps87ml72r0NSdZ_6kWyG0-pJIwbmuO2hsplWEVtZxJew-WPX_PQQa4aJFvDKKpD3oYy_zXEc2W0sDBfBEVrXXPWedcst2CzMS3Jl9la-ABLcbQNnRa2gTRSvA0bT2oQ7sCgTsGd-fDJ9H7yEBMOEXYkKUNvTNIRUhLGScWR5LQlCTy7Dj8gd8ncJUcad9Au_Dw7veqf0wZkgYaM53fUi1CxLKJYB11KE73ObZkHZ1iZ6tr4Eh-riNsK5JtWEeXVlZV1WRatCAave7AyGo_iRyBlxpVzQnjpuYxGOhENGoTCqrLyzPEusJbaRWgqkCcgjNtiUTs5Ua9A6hWJeoXswtH8k7-z8huvdT5oWVg0kjgtBG63lVBZnnXhuGXZ4vV_B_v0pt77sI6WlJ2dITuAlbvJfTyEtfCADJz0YFn__tOD1ZPTwfAS7y40xfYb66dWfMd2qG569TJ-BP9d5gA
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1NT9wwEB0hqEQ5QEtbsQVaH8qFyiJ2nNg-IISgCLTbVQ9U4hZsxzmhXboLVP1T_Y3M5GMXKsGNQ6-JYyWZ55nxjGcewBeTxEq4qLhOhONKh4p76y33mQq5zbUTvu6uP9DDobm4sD8W4G9XC0PHKjudWCvqchwoRr4ncduUySzN04PrX5xYoyi72lFoNLDoxz-_ccs23T87RvnuSHny7fzolLesAjykIr_hXoYqSSPiOOhSmeh1bss8OJOU1MjFl3g5i-hH44vqLCJAXVlZl6bRymAcBUBR5S-p1GhaV33NZ1mLPGtJdE3C0RKLLotal-oJKoRJhOE5bsq4emwH587tP_nY2sydrP1vP-gNrLYONTtsVsBbWIijdVjryCpYq7vWYeVB58V3MKwLj5vMBZveTu4isS_hQEZ1iWNGB2dZGJNhZxSqZkQZXiddENNsFohkbRDsPfx8kW_8AIuj8ShuACtTkTknpVdeqGiUk9GgGyxtVlY-caIHSSfoIrR914n-46qYd4wmbBSIjYKwUage7M4euW6ajjw3eKvDQ9Hqn2kxB0MPvnaImt9-crKPz0_2GZZPz78PisHZsL8Jr9F5tM2xuS1YvJncxm14Fe5QepNP9cJgcPnSQLsHXvlICQ
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9wwEB4hWqH2AAVadXm0PtALyCJ27MQ-IISgqyLQag9FQlyC7Tinane7C1T9a_11ncljt0UqNw69Jo6VxJ9nxvP6APZMEivhouJ5IhxXeai4t95yr1XIbJY74evu-pf5YGCur-1wCX51tTCUVtnJxFpQl-NAPvJDiccmLXWapYdVmxYxPOsfT75zYpCiSGtHp9FA5CL-_IHHt9nR-Rmu9Scp-5-_nn7hLcMAD6nI7riXoUrSiJgOealM9Hlmyyw4k5TU1MWXeFlHtKnxpXMdEayurKxL02hlMI6coSj-X-R4xqR0wqG-mUcwMt0S6pqEo1YWXUS1LtsTVBSTCMMzPKBx9bdOXBi6j2Kztcrrr_3PP-sNrLaGNjtpdsY6LMXRBqx1JBaslWkb8PqPjoybMKgLkpuIBpvdTx8isTLhQEb1imNGCbUsjEnhM3JhM6ISr4MxiHU2d1Cy1jn2Fq6e5RvfwfJoPIrvgZWp0M5J6ZUXKhrlZDRoHkury8onTvQg6Ra9CG0_dqIF-VYsOkkTTgrESUE4KVQP9uePTJpmJE8N3umwUbRyaVYsgNGDgw5di9v_nGzr6ck-wgriq7g8H1xswyu0KW2TTbcDy3fT-7gLL8MDLt70Q71HGNw-N85-A00fUS0
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=Multisource+surveillance+video+data+coding+with+hierarchical+knowledge+library&rft.jtitle=Multimedia+tools+and+applications&rft.au=Chen%2C+Yu&rft.au=Hu%2C+Ruimin&rft.au=Xiao%2C+Jing&rft.au=Xu%2C+Liang&rft.date=2019-06-01&rft.issn=1380-7501&rft.eissn=1573-7721&rft.volume=78&rft.issue=11&rft.spage=14705&rft.epage=14731&rft_id=info:doi/10.1007%2Fs11042-018-6825-4&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s11042_018_6825_4
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1380-7501&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1380-7501&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1380-7501&client=summon