{"id":"https://openalex.org/W2768634781","doi":"https://doi.org/10.1109/iccvw.2017.233","title":"Correlation Filters with Weighted Convolution Responses","display_name":"Correlation Filters with Weighted Convolution Responses","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2768634781","doi":"https://doi.org/10.1109/iccvw.2017.233","mag":"2768634781"},"language":"en","primary_location":{"id":"doi:10.1109/iccvw.2017.233","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2017.233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","raw_type":"proceedings-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/A5076998441","display_name":"Zhiqun He","orcid":"https://orcid.org/0009-0004-8734-2931"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhiqun He","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072087245","display_name":"Yingruo Fan","orcid":"https://orcid.org/0000-0001-8977-4958"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingruo Fan","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090297707","display_name":"Junfei Zhuang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfei Zhuang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056136429","display_name":"Yuan Dong","orcid":"https://orcid.org/0009-0004-8650-1603"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Dong","raw_affiliation_strings":["Beijing FaceAll Co","Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing FaceAll Co","institution_ids":[]},{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101170186","display_name":"Hongliang Bai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"HongLiang Bai","raw_affiliation_strings":["Beijing FaceAll Co"],"affiliations":[{"raw_affiliation_string":"Beijing FaceAll Co","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5076998441"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":7.2028,"has_fulltext":false,"cited_by_count":136,"citation_normalized_percentile":{"value":0.98157645,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1992","last_page":"2000"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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":1.0,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9754999876022339,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9753000140190125,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.9212085008621216},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7288706302642822},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6773733496665955},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6730162501335144},{"id":"https://openalex.org/keywords/bittorrent-tracker","display_name":"BitTorrent tracker","score":0.6613235473632812},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6050123572349548},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.589889645576477},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4983689785003662},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.46965089440345764},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.441975474357605},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4412960708141327},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4372154176235199},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.37240469455718994},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.305162250995636},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2541995048522949},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09953352808952332}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9212085008621216},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7288706302642822},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6773733496665955},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6730162501335144},{"id":"https://openalex.org/C57501372","wikidata":"https://www.wikidata.org/wiki/Q2021268","display_name":"BitTorrent tracker","level":3,"score":0.6613235473632812},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6050123572349548},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.589889645576477},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4983689785003662},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.46965089440345764},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.441975474357605},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4412960708141327},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4372154176235199},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.37240469455718994},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.305162250995636},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2541995048522949},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09953352808952332},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvw.2017.233","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2017.233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7799999713897705,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W161114242","https://openalex.org/W823292820","https://openalex.org/W1686810756","https://openalex.org/W1900239115","https://openalex.org/W1955741794","https://openalex.org/W1964846093","https://openalex.org/W1997121481","https://openalex.org/W2012231377","https://openalex.org/W2037495825","https://openalex.org/W2044986361","https://openalex.org/W2089961441","https://openalex.org/W2098841537","https://openalex.org/W2130026429","https://openalex.org/W2154889144","https://openalex.org/W2161969291","https://openalex.org/W2186330282","https://openalex.org/W2244956674","https://openalex.org/W2513005088","https://openalex.org/W2518013266","https://openalex.org/W2520438127","https://openalex.org/W2520477759","https://openalex.org/W2557641257","https://openalex.org/W2605381261","https://openalex.org/W2609910506","https://openalex.org/W2616960207","https://openalex.org/W2917435394","https://openalex.org/W2964111344","https://openalex.org/W3102624093","https://openalex.org/W3125612102","https://openalex.org/W6637373629","https://openalex.org/W6649598916","https://openalex.org/W6679027886","https://openalex.org/W6683411478","https://openalex.org/W6726083677","https://openalex.org/W6726293469","https://openalex.org/W6727158897","https://openalex.org/W6730653273","https://openalex.org/W6736018854","https://openalex.org/W6737423063","https://openalex.org/W6737982861"],"related_works":["https://openalex.org/W2803618243","https://openalex.org/W256589335","https://openalex.org/W3020706491","https://openalex.org/W2012200063","https://openalex.org/W2804764393","https://openalex.org/W4385454113","https://openalex.org/W4386114301","https://openalex.org/W4386158955","https://openalex.org/W4384788979","https://openalex.org/W178060743"],"abstract_inverted_index":{"In":[0,36,58],"recent":[1],"years,":[2],"discriminative":[3,27,51],"correlation":[4,23],"filters":[5,24],"based":[6,45,110],"trackers":[7,147],"have":[8],"shown":[9],"dominant":[10],"results":[11],"for":[12,129],"visual":[13],"object":[14],"tracking.":[15],"Combining":[16],"the":[17,22,26,50,54,71,79,91,100,112,119,124,137,145,153],"online":[18],"learning":[19],"efficiency":[20],"of":[21,29,70],"with":[25],"power":[28,52],"CNN":[30,55,74],"features":[31],"has":[32],"aroused":[33],"great":[34],"attention.":[35],"this":[37,96],"paper,":[38],"we":[39,61,122],"derive":[40],"a":[41],"continuous":[42,130],"convolution":[43,81],"operator":[44],"tracker":[46,109,141,155],"which":[47,133],"fully":[48],"exploits":[49],"in":[53],"feature":[56,65,85],"representations.":[57],"our":[59,107,140],"work,":[60],"normalize":[62],"each":[63,84],"individual":[64],"extracted":[66],"from":[67,83],"different":[68],"layers":[69],"deep":[72],"pre-trained":[73],"first,":[75],"and":[76,151],"after":[77],"that,":[78],"weighted":[80,97],"responses":[82],"block":[86],"are":[87],"summed":[88],"to":[89,136],"produce":[90],"final":[92],"confidence":[93],"score.":[94],"By":[95],"sum":[98],"operation,":[99],"empirical":[101],"evaluations":[102],"demonstrate":[103],"clear":[104],"improvements":[105],"by":[106],"proposed":[108],"on":[111,148,156],"Efficient":[113],"Convolution":[114],"Operators":[115],"Tracker":[116],"(ECO).":[117],"On":[118],"other":[120],"hand,":[121],"find":[123],"10-layers":[125],"design":[126],"is":[127],"optimal":[128],"scale":[131],"estimation,":[132],"contribute":[134],"most":[135],"performance.":[138],"Finally,":[139],"ranks":[142],"top":[143],"among":[144],"state-of-the-art":[146],"VOT2016":[149],"dataset":[150],"outperforms":[152],"ECO":[154],"VOT2017":[157],"dataset.":[158]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":27},{"year":2020,"cited_by_count":25},{"year":2019,"cited_by_count":37},{"year":2018,"cited_by_count":16}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
