{"id":"https://openalex.org/W3192870088","doi":"https://doi.org/10.1109/tcsvt.2021.3101591","title":"Bayesian Correlation Filter Learning With Gaussian Scale Mixture Model for Visual Tracking","display_name":"Bayesian Correlation Filter Learning With Gaussian Scale Mixture Model for Visual Tracking","publication_year":2021,"publication_date":"2021-07-30","ids":{"openalex":"https://openalex.org/W3192870088","doi":"https://doi.org/10.1109/tcsvt.2021.3101591","mag":"3192870088"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2021.3101591","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2021.3101591","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems for Video Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101989491","display_name":"Yuan Cao","orcid":"https://orcid.org/0000-0002-2495-4701"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuan Cao","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101549504","display_name":"Guangming Shi","orcid":"https://orcid.org/0000-0003-2179-3292"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangming Shi","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0003-2179-3292","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100648981","display_name":"Tianzhu Zhang","orcid":"https://orcid.org/0000-0003-1856-9564"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianzhu Zhang","raw_affiliation_strings":["Department of Automation, School of Information Science and Technology, University of Science and Technology of China, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0003-1856-9564","affiliations":[{"raw_affiliation_string":"Department of Automation, School of Information Science and Technology, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037310802","display_name":"Weisheng Dong","orcid":"https://orcid.org/0000-0002-9632-985X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weisheng Dong","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-9632-985X","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074780876","display_name":"Jinjian Wu","orcid":"https://orcid.org/0000-0001-7501-0009"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinjian Wu","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0001-7501-0009","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100458706","display_name":"Xuemei Xie","orcid":"https://orcid.org/0000-0001-7857-0845"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuemei Xie","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0001-7857-0845","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100354039","display_name":"Xin Li","orcid":"https://orcid.org/0000-0003-2067-2763"},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Li","raw_affiliation_strings":["Lane Department of CSEE, West Virginia University, Morgantown, WV, USA"],"raw_orcid":"https://orcid.org/0000-0003-2067-2763","affiliations":[{"raw_affiliation_string":"Lane Department of CSEE, West Virginia University, Morgantown, WV, USA","institution_ids":["https://openalex.org/I12097938"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101989491"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.9709,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.77638999,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"32","issue":"5","first_page":"3085","last_page":"3098"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9749000072479248,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9743000268936157,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5185823440551758},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5037328600883484},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4754706919193268},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.47205430269241333},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4437946379184723},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.43385520577430725},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43044379353523254},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.41560953855514526},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39036938548088074}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5185823440551758},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5037328600883484},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4754706919193268},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.47205430269241333},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4437946379184723},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.43385520577430725},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43044379353523254},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.41560953855514526},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39036938548088074},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2021.3101591","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2021.3101591","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6899999976158142,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1586809628","display_name":null,"funder_award_id":"2018AAA0101400","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G1725003993","display_name":null,"funder_award_id":"61621005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4150153631","display_name":null,"funder_award_id":"61836008","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6862244890","display_name":null,"funder_award_id":"61632019","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G712080188","display_name":null,"funder_award_id":"61991451","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":98,"referenced_works":["https://openalex.org/W45374770","https://openalex.org/W182940129","https://openalex.org/W818325216","https://openalex.org/W1857884451","https://openalex.org/W1892578678","https://openalex.org/W1915785815","https://openalex.org/W1955514522","https://openalex.org/W1955741794","https://openalex.org/W1963882359","https://openalex.org/W1964846093","https://openalex.org/W1997121481","https://openalex.org/W1998966727","https://openalex.org/W2089961441","https://openalex.org/W2111557737","https://openalex.org/W2113945798","https://openalex.org/W2118345389","https://openalex.org/W2152652402","https://openalex.org/W2154889144","https://openalex.org/W2156273867","https://openalex.org/W2158592639","https://openalex.org/W2161969291","https://openalex.org/W2162349892","https://openalex.org/W2211807644","https://openalex.org/W2214352687","https://openalex.org/W2244956674","https://openalex.org/W2344989787","https://openalex.org/W2469582947","https://openalex.org/W2470394683","https://openalex.org/W2470648425","https://openalex.org/W2473868734","https://openalex.org/W2474516676","https://openalex.org/W2493977830","https://openalex.org/W2513005088","https://openalex.org/W2518013266","https://openalex.org/W2520477759","https://openalex.org/W2557641257","https://openalex.org/W2558899534","https://openalex.org/W2587145170","https://openalex.org/W2593128366","https://openalex.org/W2599547527","https://openalex.org/W2603078356","https://openalex.org/W2605381261","https://openalex.org/W2606879377","https://openalex.org/W2609910506","https://openalex.org/W2673818281","https://openalex.org/W2738318237","https://openalex.org/W2740685955","https://openalex.org/W2742165450","https://openalex.org/W2776035257","https://openalex.org/W2792121015","https://openalex.org/W2798842862","https://openalex.org/W2799058067","https://openalex.org/W2805950667","https://openalex.org/W2886910176","https://openalex.org/W2890678738","https://openalex.org/W2898020386","https://openalex.org/W2901259746","https://openalex.org/W2906497434","https://openalex.org/W2906580494","https://openalex.org/W2907368145","https://openalex.org/W2909529016","https://openalex.org/W2912465472","https://openalex.org/W2912512021","https://openalex.org/W2912636251","https://openalex.org/W2916780012","https://openalex.org/W2917435394","https://openalex.org/W2920748649","https://openalex.org/W2937749627","https://openalex.org/W2954137266","https://openalex.org/W2955747520","https://openalex.org/W2955983623","https://openalex.org/W2962972233","https://openalex.org/W2963030525","https://openalex.org/W2963099472","https://openalex.org/W2963173190","https://openalex.org/W2963272600","https://openalex.org/W2963534981","https://openalex.org/W2963571423","https://openalex.org/W2963854930","https://openalex.org/W2964069521","https://openalex.org/W2964111344","https://openalex.org/W2964242925","https://openalex.org/W2965849559","https://openalex.org/W2979087743","https://openalex.org/W2987346479","https://openalex.org/W2989982877","https://openalex.org/W2992224095","https://openalex.org/W2997104867","https://openalex.org/W3006757851","https://openalex.org/W3012509084","https://openalex.org/W3018207438","https://openalex.org/W3084886667","https://openalex.org/W3097992810","https://openalex.org/W3101525822","https://openalex.org/W3135450171","https://openalex.org/W6726083677","https://openalex.org/W6736018854","https://openalex.org/W6910333822"],"related_works":["https://openalex.org/W2729514902","https://openalex.org/W2024160000","https://openalex.org/W2773500201","https://openalex.org/W2061273563","https://openalex.org/W2285052147","https://openalex.org/W4287995534","https://openalex.org/W2743258233","https://openalex.org/W2998168123","https://openalex.org/W1972656095","https://openalex.org/W2321141263"],"abstract_inverted_index":{"Correlation":[0],"filters":[1,31],"(CF),":[2],"a":[3,38,73,99,104,120,145,159],"popular":[4],"tool":[5],"for":[6,20,68],"visual":[7],"tracking,":[8,70],"suffer":[9],"from":[10],"unwanted":[11],"boundary":[12],"effects":[13],"due":[14],"to":[15,41,90,119,169],"the":[16,42,46,96,149,172,180],"periodic":[17],"assumption":[18],"needed":[19],"FFT":[21],"implementation.":[22],"To":[23],"address":[24],"this":[25],"issue,":[26],"spatially":[27,121],"regularized":[28],"discriminative":[29],"correlation":[30,69,76],"(SRDCF)":[32],"have":[33],"been":[34],"proposed":[35],"by":[36,59,143],"introducing":[37],"weighting":[39,51,112],"matrix":[40,52],"regularization":[43,123],"term.":[44],"However,":[45],"existing":[47],"design":[48],"of":[49,98,116,128],"spatial":[50,173],"is":[53,89],"often":[54],"heuristic":[55],"and":[56,65,103,139,156,177,191],"non-adaptive.":[57],"Inspired":[58],"recent":[60],"advances":[61],"in":[62],"joint":[63],"discrimination":[64],"reliability":[66],"learning":[67,78],"we":[71,151],"propose":[72],"principled":[74],"Bayesian":[75,161],"filter":[77],"method":[79,197],"using":[80],"Gaussian":[81,105],"scale":[82,135],"mixture":[83],"(GSM)":[84],"model.":[85],"The":[86],"key":[87],"idea":[88],"decompose":[91],"each":[92],"CF":[93],"coefficient":[94],"into":[95],"product":[97],"positive":[100,109],"scalar":[101],"multiplier":[102],"random":[106],"variable.":[107],"Treating":[108],"multipliers":[110,155],"as":[111],"coefficients,":[113],"GSM-based":[114],"modeling":[115],"CFs":[117,157,176],"leads":[118],"adaptive":[122],"strategy":[124],"with":[125,202],"improved":[126],"capability":[127],"handling":[129],"various":[130],"appearance-related":[131],"uncertainty":[132],"factors":[133],"(e.g.,":[134],"variation,":[136],"out-of-plane":[137],"rotation,":[138],"motion":[140],"blur).":[141],"Moreover,":[142],"imposing":[144],"sparse":[146],"prior":[147],"over":[148],"multipliers,":[150],"can":[152],"jointly":[153],"learn":[154],"under":[158],"unified":[160],"estimation":[162],"framework.":[163],"Structured":[164],"GSM":[165],"model":[166],"allows":[167],"us":[168],"better":[170],"exploit":[171],"correlations":[174],"among":[175],"further":[178],"improve":[179],"tracking":[181,196],"performance.":[182],"Experimental":[183],"results":[184],"on":[185],"OTB-2013,":[186],"OTB-2015,":[187],"Temple":[188],"Color-128,":[189],"VOT-2016,":[190],"VOT-2017":[192],"show":[193],"that":[194],"our":[195],"performs":[198],"favorably":[199],"when":[200],"compared":[201],"current":[203],"state-of-the-art":[204],"methods.":[205]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}