Efficient Multitarget Visual Tracking Using Random Finite Sets
We propose a filtering framework for multitarget tracking that is based on the probability hypothesis density (PHD) filter and data association using graph matching. This framework can be combined with any object detectors that generate positional and dimensional information of objects of interest....
Uložené v:
| Vydané v: | IEEE transactions on circuits and systems for video technology Ročník 18; číslo 8; s. 1016 - 1027 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
IEEE
01.08.2008
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 1051-8215, 1558-2205 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | We propose a filtering framework for multitarget tracking that is based on the probability hypothesis density (PHD) filter and data association using graph matching. This framework can be combined with any object detectors that generate positional and dimensional information of objects of interest. The PHD filter compensates for missing detections and removes noise and clutter. Moreover, this filter reduces the growth in complexity with the number of targets from exponential to linear by propagating the first-order moment of the multitarget posterior, instead of the full posterior. In order to account for the nature of the PHD propagation, we propose a novel particle resampling strategy and we adapt dynamic and observation models to cope with varying object scales. The proposed resampling strategy allows us to use the PHD filter when a priori knowledge of the scene is not available. Moreover, the dynamic and observation models are not limited to the PHD filter and can be applied to any Bayesian tracker that can handle state-dependent variances. Extensive experimental results on a large video surveillance dataset using a standard evaluation protocol show that the proposed filtering framework improves the accuracy of the tracker, especially in cluttered scenes. |
|---|---|
| AbstractList | The PHD filter compensates for missing detections and removes noise and clutter. [...] this filter reduces the growth in complexity with the number of targets from exponential to linear by propagating the first-order moment of the multitarget posterior, instead of the full posterior. We propose a filtering framework for multitarget tracking that is based on the probability hypothesis density (PHD) filter and data association using graph matching. This framework can be combined with any object detectors that generate positional and dimensional information of objects of interest. The PHD filter compensates for missing detections and removes noise and clutter. Moreover, this filter reduces the growth in complexity with the number of targets from exponential to linear by propagating the first-order moment of the multitarget posterior, instead of the full posterior. In order to account for the nature of the PHD propagation, we propose a novel particle resampling strategy and we adapt dynamic and observation models to cope with varying object scales. The proposed resampling strategy allows us to use the PHD filter when @@ia priori@ knowledge of the scene is not available. Moreover, the dynamic and observation models are not limited to the PHD filter and can be applied to any Bayesian tracker that can handle state-dependent variances. Extensive experimental results on a large video surveillance dataset using a standard evaluation protocol show that the proposed filtering framework improves the accuracy of the tracker, especially in cluttered scenes. We propose a filtering framework for multitarget tracking that is based on the probability hypothesis density (PHD) filter and data association using graph matching. This framework can be combined with any object detectors that generate positional and dimensional information of objects of interest. The PHD filter compensates for missing detections and removes noise and clutter. Moreover, this filter reduces the growth in complexity with the number of targets from exponential to linear by propagating the first-order moment of the multitarget posterior, instead of the full posterior. In order to account for the nature of the PHD propagation, we propose a novel particle resampling strategy and we adapt dynamic and observation models to cope with varying object scales. The proposed resampling strategy allows us to use the PHD filter when a priori knowledge of the scene is not available. Moreover, the dynamic and observation models are not limited to the PHD filter and can be applied to any Bayesian tracker that can handle state-dependent variances. Extensive experimental results on a large video surveillance dataset using a standard evaluation protocol show that the proposed filtering framework improves the accuracy of the tracker, especially in cluttered scenes. |
| Author | Taj, M. Cavallaro, A. Maggio, E. |
| Author_xml | – sequence: 1 givenname: E. surname: Maggio fullname: Maggio, E. organization: Multimedia & Vision Group, Univ. of London, London – sequence: 2 givenname: M. surname: Taj fullname: Taj, M. organization: Multimedia & Vision Group, Univ. of London, London – sequence: 3 givenname: A. surname: Cavallaro fullname: Cavallaro, A. organization: Multimedia & Vision Group, Univ. of London, London |
| BookMark | eNp9kLtOwzAUhi1UJNrCAyCWiIGtxZecJF6QUNUCUhESvayW69iVS-oU2xl4e1KCGDqw-Hj4vnP5B6jnaqcRuiZ4TAjm98vJYr0cU4yLMacFpeQM9QlAMaIUQ6_9YyCjghK4QIMQdhiTtEjzPnqYGmOV1S4mr00VbZR-q2OytqGRVbL0Un1Yt01W4fi-S1fW-2RmnY06WegYLtG5kVXQV791iFaz6XLyPJq_Pb1MHucjxSiL7WAGRcoympENz8sScp0boGaD1QZTSdKMaMgxM0pRjYGWrMSsBANlZigvORuiu67vwdefjQ5R7G1Quqqk03UTBEtTznJMWvD2BNzVjXftboITSgvgGFoo7yDl6xC8NkK1h0dbu-ilrQTB4hiq-AlVHEMVXaitSU7Mg7d76b_-dW46x2qt__gUgAPP2TcPgoNF |
| CODEN | ITCTEM |
| CitedBy_id | crossref_primary_10_1109_TCSVT_2013_2249374 crossref_primary_10_1016_j_inffus_2016_02_004 crossref_primary_10_1155_2014_495765 crossref_primary_10_1016_j_jvcir_2019_01_026 crossref_primary_10_1016_j_dsp_2019_03_005 crossref_primary_10_1016_j_inffus_2011_03_005 crossref_primary_10_1109_MSP_2010_937395 crossref_primary_10_1121_10_0002257 crossref_primary_10_3390_s16091399 crossref_primary_10_1016_j_sigpro_2013_08_002 crossref_primary_10_1109_JSEN_2023_3285885 crossref_primary_10_1109_TSP_2012_2222389 crossref_primary_10_3724_SP_J_1004_2010_00731 crossref_primary_10_1016_j_sigpro_2019_107278 crossref_primary_10_1109_TCYB_2019_2912939 crossref_primary_10_1049_iet_rsn_2014_0467 crossref_primary_10_1155_2014_481719 crossref_primary_10_1109_TII_2013_2294156 crossref_primary_10_3390_s20030929 crossref_primary_10_1109_ACCESS_2019_2904545 crossref_primary_10_1016_j_neucom_2015_05_096 crossref_primary_10_3390_e23050628 crossref_primary_10_1007_s00138_018_0984_1 crossref_primary_10_1109_TSP_2019_2943234 crossref_primary_10_1016_j_sigpro_2013_04_016 crossref_primary_10_1016_j_ast_2019_02_004 crossref_primary_10_1049_iet_cvi_2010_0026 crossref_primary_10_1155_2014_653259 crossref_primary_10_3724_SP_J_1004_2010_00939 crossref_primary_10_1016_j_cviu_2016_07_006 crossref_primary_10_1016_j_cviu_2016_07_003 crossref_primary_10_1016_j_sigpro_2019_01_028 crossref_primary_10_1007_s00285_015_0909_9 crossref_primary_10_3390_s19061307 crossref_primary_10_3390_app12031369 crossref_primary_10_1109_TAES_2011_5751278 crossref_primary_10_1109_TCSVT_2017_2720749 crossref_primary_10_1109_TVT_2022_3174055 crossref_primary_10_1109_TGRS_2013_2286834 crossref_primary_10_1109_TPAMI_2020_3034435 crossref_primary_10_1186_1687_6180_2011_130 crossref_primary_10_3390_s17030501 crossref_primary_10_1109_TAES_2019_2921210 crossref_primary_10_3390_electronics8070741 crossref_primary_10_1109_TCSVT_2011_2177937 crossref_primary_10_1109_TSP_2014_2364014 crossref_primary_10_1109_TCSVT_2015_2409632 crossref_primary_10_1145_2530282 crossref_primary_10_3390_s20123384 crossref_primary_10_1109_JSTARS_2012_2191144 crossref_primary_10_1109_TAES_2010_5595616 crossref_primary_10_3390_s23218751 crossref_primary_10_1109_LSP_2016_2611138 crossref_primary_10_1109_TSP_2015_2454478 crossref_primary_10_1016_j_jvcir_2015_06_015 crossref_primary_10_3390_s19245437 crossref_primary_10_1109_TMM_2016_2638206 crossref_primary_10_1155_2013_727430 crossref_primary_10_3390_s151229794 crossref_primary_10_1016_j_patcog_2013_02_013 crossref_primary_10_1016_j_asr_2017_03_002 crossref_primary_10_1109_ACCESS_2018_2816805 crossref_primary_10_1109_TCSVT_2017_2736553 crossref_primary_10_1109_TAES_2015_140211 crossref_primary_10_1109_TCSVT_2015_2416555 crossref_primary_10_1121_10_0006780 crossref_primary_10_1007_s11432_013_4975_6 crossref_primary_10_1016_j_ijleo_2015_06_020 crossref_primary_10_1109_TSP_2015_2468677 crossref_primary_10_1109_TIP_2012_2210238 crossref_primary_10_1016_j_neucom_2017_10_018 crossref_primary_10_1049_iet_rsn_2010_0057 crossref_primary_10_1109_TSP_2016_2641392 crossref_primary_10_1016_j_sigpro_2013_03_004 crossref_primary_10_1016_j_ijleo_2013_10_108 crossref_primary_10_1016_j_sigpro_2018_04_015 crossref_primary_10_1109_JSEN_2015_2446756 crossref_primary_10_1109_TAES_2012_6178085 crossref_primary_10_1109_TPAMI_2011_21 crossref_primary_10_1109_TIP_2009_2019934 |
| Cites_doi | 10.1109/CDC.2006.377103 10.1109/TPAMI.2005.1 10.1109/ICASSP.2007.366104 10.1109/TAC.1979.1102177 10.1109/ICCV.2001.937594 10.1023/A:1008078328650 10.1155/S1110865704402157 10.1109/TAES.2005.1413764 10.1109/ICPR.2006.1131 10.1109/78.978374 10.1137/0202019 10.1109/TSP.2006.881190 10.1109/TAES.2007.357143 10.1109/ICIF.2006.301809 10.1023/B:VISI.0000013087.49260.fb 10.1109/TAES.2002.1039400 10.1016/j.inffus.2005.09.009 10.1109/ICIF.2003.177320 10.1109/ICIF.2002.1021192 10.1109/TAES.2007.4285353 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D F28 FR3 |
| DOI | 10.1109/TCSVT.2008.928221 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present 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 ANTE: Abstracts in New Technology & Engineering Engineering Research Database |
| 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 Engineering Research Database ANTE: Abstracts in New Technology & Engineering |
| DatabaseTitleList | Technology Research Database Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1558-2205 |
| EndPage | 1027 |
| ExternalDocumentID | 2545208411 10_1109_TCSVT_2008_928221 4559597 |
| Genre | orig-research |
| 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 RIG F28 FR3 |
| ID | FETCH-LOGICAL-c323t-82358436261b97dd57e7f52fb0cb02a1461e5703fcc2e052d3d03d5f5d6f29d93 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 123 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000259573700003&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 | Sat Sep 27 22:22:52 EDT 2025 Mon Jun 30 05:17:44 EDT 2025 Sat Nov 29 01:43:58 EST 2025 Tue Nov 18 22:38:11 EST 2025 Tue Aug 26 16:47:33 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8 |
| Keywords | Surveillance Monte Carlo tracking PHD filter |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c323t-82358436261b97dd57e7f52fb0cb02a1461e5703fcc2e052d3d03d5f5d6f29d93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 |
| PQID | 912285905 |
| PQPubID | 85433 |
| PageCount | 12 |
| ParticipantIDs | crossref_citationtrail_10_1109_TCSVT_2008_928221 proquest_journals_912285905 ieee_primary_4559597 crossref_primary_10_1109_TCSVT_2008_928221 proquest_miscellaneous_34493701 |
| PublicationCentury | 2000 |
| PublicationDate | 2008-Aug. 2008-08-00 20080801 |
| PublicationDateYYYYMMDD | 2008-08-01 |
| PublicationDate_xml | – month: 08 year: 2008 text: 2008-Aug. |
| PublicationDecade | 2000 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on circuits and systems for video technology |
| PublicationTitleAbbrev | TCSVT |
| PublicationYear | 2008 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 sidenbladh (ref18) 2003 khan (ref6) 2004 ref15 ref14 ref11 ref10 ref2 ref1 ref17 ref16 ref19 bar-shalom (ref3) 1988 ref24 ref23 ref26 okuma (ref4) 2004 ref25 ref22 ref21 ikoma (ref20) 2004; 2 kasturi (ref27) 2006 ref8 ref7 ref9 ref5 mahler (ref12) 2002; 1 |
| References_xml | – ident: ref17 doi: 10.1109/CDC.2006.377103 – ident: ref11 doi: 10.1109/TPAMI.2005.1 – year: 2006 ident: ref27 publication-title: Performance Evaluation Protocol for Face Person and Vehicle Detection & Tracking in Video Analysis and Content Extraction – start-page: 98 year: 2003 ident: ref18 article-title: tracking random sets of vehicles in terrain publication-title: Proc IEEE Workshop on Multi-Object Tracking – ident: ref22 doi: 10.1109/ICASSP.2007.366104 – ident: ref25 doi: 10.1109/TAC.1979.1102177 – ident: ref5 doi: 10.1109/ICCV.2001.937594 – ident: ref1 doi: 10.1023/A:1008078328650 – ident: ref23 doi: 10.1155/S1110865704402157 – start-page: 279 year: 2004 ident: ref6 article-title: an mcmc-based particle filter for tracking multiple interacting targets publication-title: Proc Eur Conf Comput Vis – ident: ref9 doi: 10.1109/TAES.2005.1413764 – ident: ref21 doi: 10.1109/ICPR.2006.1131 – start-page: 28 year: 2004 ident: ref4 article-title: a boosted particle filter: multitarget detection and tracking publication-title: Proc Eur Conf Comput Vis – volume: 2 start-page: 1696 year: 2004 ident: ref20 article-title: tracking of feature points in image sequence by smc implementation of phd filter publication-title: Proc SICE Annual Conf – ident: ref2 doi: 10.1109/78.978374 – ident: ref26 doi: 10.1137/0202019 – year: 1988 ident: ref3 publication-title: Tracking and Data Association – ident: ref16 doi: 10.1109/TSP.2006.881190 – ident: ref19 doi: 10.1109/TAES.2007.357143 – ident: ref14 doi: 10.1109/ICIF.2006.301809 – ident: ref24 doi: 10.1023/B:VISI.0000013087.49260.fb – ident: ref7 doi: 10.1109/TAES.2002.1039400 – ident: ref10 doi: 10.1016/j.inffus.2005.09.009 – ident: ref13 doi: 10.1109/ICIF.2003.177320 – ident: ref8 doi: 10.1109/ICIF.2002.1021192 – ident: ref15 doi: 10.1109/TAES.2007.4285353 – volume: 1 year: 2002 ident: ref12 article-title: a theoretical foundation for the stein-winter probability hypothesis density (phd) multitarget tracking approach publication-title: Proc MSS Nat Symp Sensor Data Fusion |
| SSID | ssj0014847 |
| Score | 2.344781 |
| Snippet | We propose a filtering framework for multitarget tracking that is based on the probability hypothesis density (PHD) filter and data association using graph... The PHD filter compensates for missing detections and removes noise and clutter. [...] this filter reduces the growth in complexity with the number of targets... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1016 |
| SubjectTerms | Bayesian methods Detectors Filtering Layout Matched filters Monte Carlo Nonlinear filters Object detection Particle filters probability hypothesis density (PHD) filter Studies surveillance Target tracking tracking Video surveillance |
| Title | Efficient Multitarget Visual Tracking Using Random Finite Sets |
| URI | https://ieeexplore.ieee.org/document/4559597 https://www.proquest.com/docview/912285905 https://www.proquest.com/docview/34493701 |
| Volume | 18 |
| WOSCitedRecordID | wos000259573700003&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 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_JrinB89eBLrkjRtlosgY8ODDHFz7FbaJIWBtrJu_v3moyuKIngrJK-0L0nfr3m__B7AVSZwoChDPtNY2qeGIZbgSPoSSckixFPqRJIe2Xjcn8_5UwNu6rMwSilLPlO35tLm8mUh1marrEc1_NUAuAlNxiJ3VqvOGNC-LSam4QL2-zqOVRlMjHhvOpjMpo42yQ1rEn-LQbaoyo8vsQ0vo73_Pdg-7FYw0rt3434ADZUfws4XccE23A2tOoQ29NwpW8v59maLcq0tdYwSZpfcs5wB7znJZfHmjRYGgnoTtSqP4GU0nA4e_Kpagi8CEqz0SwcaTBh1GZxyJmXIFMtCkqVIpIgkpn63MnJbmRBEoZDIQKJAhlkoo4xwyYNjaOVFrk7A47o9RBnvkxTThJM0TSKaYcoYlijlsgNo479YVFLipqLFa2x_KRCPrctdiUvn8g5c1ybvTkfjr85t4-O6Y-XeDnQ3gxRXK62MOSZGgw-FHbisW_USMXmPJFfFuowDSjUIQ_j099t2YduRQAyr7wxaq-VancOW-FgtyuWFnWWfaCvN0w |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1dS8MwFL3MKagPfotz6vrgk1iXpOmyvAgyNibOIW6OvZU2SWGgraybv98k7YqiCL4V0pT2puk9zT05B-AyFthTlCGXaSztUsMQC3FLuhJJyVqIRzQXSRqw4bA9nfKnClyXe2GUUpZ8pm7Moa3ly1QszVJZk2r4qwHwGqwb56xit1ZZM6BtayemAQN22zqTFTVMjHhz3BlNxjlxkhveJP6Whaytyo9vsU0wvd3_3doe7BRA0rnLR34fKio5gO0v8oKHcNu1-hC6o5Pvs7Wsb2cyy5a6p85SwqyTO5Y14DyHiUzfnN7MgFBnpBbZEbz0uuNO3y38ElzhEW-hH9rTcMLoy-CIMyl9pljskzhCIkIkNA7eyghuxUIQhXwiPYk86ce-bMWES-4dQzVJE3UCDtftPop5m0SYhpxEUdiiMaaMYYkiLmuAVvELRCEmbjwtXgP7U4F4YEOem1zmIa_BVdnlPVfS-OvkQxPj8sQivDWorwYpKOZaFnBMjAof8mvQKFv1JDGVjzBR6TILPEo1DEP49PfLNmCzP34cBIP74UMdtnJKiOH4nUF1MV-qc9gQH4tZNr-wb9wnS8nRHA |
| 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=Efficient+Multitarget+Visual+Tracking+Using+Random+Finite+Sets&rft.jtitle=IEEE+transactions+on+circuits+and+systems+for+video+technology&rft.au=Maggio%2C+E.&rft.au=Taj%2C+M.&rft.au=Cavallaro%2C+A.&rft.date=2008-08-01&rft.issn=1051-8215&rft.eissn=1558-2205&rft.volume=18&rft.issue=8&rft.spage=1016&rft.epage=1027&rft_id=info:doi/10.1109%2FTCSVT.2008.928221&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TCSVT_2008_928221 |
| 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 |