{"id":"https://openalex.org/W2941319421","doi":"https://doi.org/10.1109/access.2019.2910835","title":"Comparative Object Similarity Learning-Based Robust Visual Tracking","display_name":"Comparative Object Similarity Learning-Based Robust Visual Tracking","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2941319421","doi":"https://doi.org/10.1109/access.2019.2910835","mag":"2941319421"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2910835","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2910835","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08693946.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08693946.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101842138","display_name":"Weiming Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I132369690","display_name":"Tianjin University of Science and Technology","ror":"https://ror.org/018rbtf37","country_code":"CN","type":"education","lineage":["https://openalex.org/I132369690"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiming Yang","raw_affiliation_strings":["College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-7101-670X","affiliations":[{"raw_affiliation_string":"College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, China","institution_ids":["https://openalex.org/I132369690"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100389402","display_name":"Yuliang Liu","orcid":"https://orcid.org/0000-0003-4308-9458"},"institutions":[{"id":"https://openalex.org/I132369690","display_name":"Tianjin University of Science and Technology","ror":"https://ror.org/018rbtf37","country_code":"CN","type":"education","lineage":["https://openalex.org/I132369690"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuliang Liu","raw_affiliation_strings":["College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, China","institution_ids":["https://openalex.org/I132369690"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100406689","display_name":"Quan Zhang","orcid":"https://orcid.org/0000-0002-9039-4140"},"institutions":[{"id":"https://openalex.org/I132369690","display_name":"Tianjin University of Science and Technology","ror":"https://ror.org/018rbtf37","country_code":"CN","type":"education","lineage":["https://openalex.org/I132369690"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quan Zhang","raw_affiliation_strings":["College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, China","institution_ids":["https://openalex.org/I132369690"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101634006","display_name":"Yelong Zheng","orcid":"https://orcid.org/0000-0002-8204-946X"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yelong Zheng","raw_affiliation_strings":["The State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-8204-946X","affiliations":[{"raw_affiliation_string":"The State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.6948,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.73979907,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"7","issue":null,"first_page":"50466","last_page":"50475"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9373000264167786,"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"}},{"id":"https://openalex.org/T11963","display_name":"Impact of Light on Environment and Health","score":0.9319000244140625,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7954601645469666},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7567296028137207},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.561325192451477},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5545814037322998},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.5359616279602051},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5072872042655945},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.5028161406517029},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4996452331542969},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.45599716901779175},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44410914182662964},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4259375035762787},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42459583282470703},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41058871150016785},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.3271256685256958},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19541484117507935}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7954601645469666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7567296028137207},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.561325192451477},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5545814037322998},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.5359616279602051},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5072872042655945},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.5028161406517029},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4996452331542969},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.45599716901779175},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44410914182662964},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4259375035762787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42459583282470703},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41058871150016785},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3271256685256958},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19541484117507935}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2910835","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2910835","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08693946.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:be0a4a82bf0d4c1e9e0a0b6ee210d910","is_oa":true,"landing_page_url":"https://doaj.org/article/be0a4a82bf0d4c1e9e0a0b6ee210d910","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 50466-50475 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2910835","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2910835","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08693946.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.550000011920929,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G1723281013","display_name":null,"funder_award_id":"51805367","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"},{"id":"https://openalex.org/G1965726802","display_name":null,"funder_award_id":"51805367","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5172241042","display_name":null,"funder_award_id":"17JCYBJC19000","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"},{"id":"https://openalex.org/G6938866086","display_name":null,"funder_award_id":"17JCYBJC19000","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/F4320323993","display_name":"Natural Science Foundation of Tianjin City","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2941319421.pdf","grobid_xml":"https://content.openalex.org/works/W2941319421.grobid-xml"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W29474918","https://openalex.org/W161114242","https://openalex.org/W182940129","https://openalex.org/W818325216","https://openalex.org/W1807914171","https://openalex.org/W1964846093","https://openalex.org/W2031489346","https://openalex.org/W2044986361","https://openalex.org/W2059781288","https://openalex.org/W2084112010","https://openalex.org/W2089961441","https://openalex.org/W2093135704","https://openalex.org/W2098941887","https://openalex.org/W2111787305","https://openalex.org/W2118345389","https://openalex.org/W2120396754","https://openalex.org/W2124211486","https://openalex.org/W2125993116","https://openalex.org/W2150621701","https://openalex.org/W2154889144","https://openalex.org/W2158592639","https://openalex.org/W2162919312","https://openalex.org/W2167089254","https://openalex.org/W2168356304","https://openalex.org/W2211629196","https://openalex.org/W2244956674","https://openalex.org/W2308665943","https://openalex.org/W2408241409","https://openalex.org/W2463288413","https://openalex.org/W2470394683","https://openalex.org/W2474599091","https://openalex.org/W2518013266","https://openalex.org/W2520477759","https://openalex.org/W2557641257","https://openalex.org/W2579238278","https://openalex.org/W2599547527","https://openalex.org/W2604679602","https://openalex.org/W2610871254","https://openalex.org/W2767101793","https://openalex.org/W2768166594","https://openalex.org/W2781948176","https://openalex.org/W2794210434","https://openalex.org/W2794941951","https://openalex.org/W2797812763","https://openalex.org/W2800452261","https://openalex.org/W2962864296","https://openalex.org/W2964111344","https://openalex.org/W3113028293","https://openalex.org/W3123635396","https://openalex.org/W3144619878","https://openalex.org/W6601205835","https://openalex.org/W6606595081","https://openalex.org/W6623108133","https://openalex.org/W6676780147","https://openalex.org/W6720898849","https://openalex.org/W6726293469","https://openalex.org/W6750996522"],"related_works":["https://openalex.org/W2375480909","https://openalex.org/W2353314428","https://openalex.org/W2012019886","https://openalex.org/W2166090428","https://openalex.org/W2381021552","https://openalex.org/W2354749003","https://openalex.org/W2377121353","https://openalex.org/W2971551846","https://openalex.org/W2965594636","https://openalex.org/W2164674712"],"abstract_inverted_index":{"Tracking-by-detection":[0],"for":[1],"visual":[2,58],"object":[3,68,87],"tracking":[4,17,31,135],"is":[5,46,83,94,106],"the":[6,16,27,38,55,74,85,91,103,109,113,121,130,134,141,155,159,173,176,186],"most":[7],"popular":[8],"and":[9,23,165,180],"successful":[10],"framework":[11],"at":[12,54],"present.":[13],"It":[14],"treats":[15],"problem":[18],"as":[19],"a":[20,65],"classification":[21],"task":[22],"learns":[24],"information":[25,89,111],"about":[26],"target":[28,114],"from":[29,140,161],"each":[30],"result":[32],"online.":[33],"Accurate":[34],"model":[35,105],"learning":[36,70],"of":[37,57,80,112,145,154,175],"classifier":[39,104],"requires":[40],"numerous":[41,50],"positive":[42,51],"samples.":[43],"However,":[44],"it":[45],"difficult":[47],"to":[48,72,115,129,171],"obtain":[49],"training":[52,75,99],"samples":[53,76],"beginning":[56],"tracking.":[59],"In":[60,101],"this":[61],"paper,":[62],"we":[63],"propose":[64],"novel":[66],"comparative":[67,86],"similarity":[69,88],"method":[71,188],"strengthen":[73],"set.":[77],"The":[78,178],"core":[79],"our":[81],"approach":[82],"that":[84,185],"between":[90,124],"candidate":[92],"objects":[93],"taken":[95],"into":[96],"account":[97],"when":[98],"classifiers.":[100],"addition,":[102],"updated":[107],"with":[108,158],"image":[110,126],"be":[116],"predicted":[117],"by":[118],"further":[119],"exploring":[120],"temporal":[122],"context":[123],"successive":[125],"frames.":[127],"According":[128],"Bayesian":[131],"inference":[132],"theorem,":[133],"results,":[136],"which":[137],"are":[138,147],"estimated":[139],"posterior":[142],"probability":[143],"distribution":[144],"target,":[146],"more":[148],"accurate.":[149],"We":[150],"implement":[151],"two":[152],"versions":[153],"proposed":[156,187],"tracker":[157],"representations":[160],"both":[162],"conventional":[163],"hand-crafted":[164],"deep":[166],"convolution":[167],"neural":[168],"networks-based":[169],"features":[170],"validate":[172],"effectiveness":[174],"algorithm.":[177],"quantitative":[179],"qualitative":[181],"experimental":[182],"results":[183],"demonstrate":[184],"performs":[189],"superiorly":[190],"against":[191],"several":[192],"state-of-the-art":[193],"algorithms":[194],"on":[195],"large-scale":[196],"challenging":[197],"benchmark":[198],"datasets.":[199]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
