{"id":"https://openalex.org/W2995399338","doi":"https://doi.org/10.1109/access.2019.2960105","title":"Robust Object Tracking Using Affine Transformation and Convolutional Features","display_name":"Robust Object Tracking Using Affine Transformation and Convolutional Features","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2995399338","doi":"https://doi.org/10.1109/access.2019.2960105","mag":"2995399338"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2960105","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2960105","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08933387.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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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/ielx7/6287639/8600701/08933387.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077013382","display_name":"Yinghong Xie","orcid":"https://orcid.org/0000-0002-4857-069X"},"institutions":[{"id":"https://openalex.org/I42852656","display_name":"Shenyang University","ror":"https://ror.org/04ddfwm68","country_code":"CN","type":"education","lineage":["https://openalex.org/I42852656"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yinghong Xie","raw_affiliation_strings":["College of Information Engineering, Shenyang University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-4857-069X","affiliations":[{"raw_affiliation_string":"College of Information Engineering, Shenyang University, Shenyang, China","institution_ids":["https://openalex.org/I42852656"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001219455","display_name":"Jie Shen","orcid":"https://orcid.org/0000-0001-5117-3655"},"institutions":[{"id":"https://openalex.org/I4210130704","display_name":"University of Michigan\u2013Dearborn","ror":"https://ror.org/035wtm547","country_code":"US","type":"education","lineage":["https://openalex.org/I4210130704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jie Shen","raw_affiliation_strings":["College of Engineering and Computer Science, University of Michigan\u2014Dearborn, Dearborn, USA"],"raw_orcid":"https://orcid.org/0000-0001-5117-3655","affiliations":[{"raw_affiliation_string":"College of Engineering and Computer Science, University of Michigan\u2014Dearborn, Dearborn, USA","institution_ids":["https://openalex.org/I4210130704"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000383993","display_name":"Chengdong Wu","orcid":"https://orcid.org/0000-0001-9906-5493"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengdong Wu","raw_affiliation_strings":["College of Faculty of Robot Science and Engineer, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0001-9906-5493","affiliations":[{"raw_affiliation_string":"College of Faculty of Robot Science and Engineer, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5077013382"],"corresponding_institution_ids":["https://openalex.org/I42852656"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.1486331,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"7","issue":null,"first_page":"182489","last_page":"182498"},"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":0.9939000010490417,"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":0.9902999997138977,"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":0.8329979181289673},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.77195143699646},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.745446503162384},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7317658066749573},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7272392511367798},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6065264344215393},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5777796506881714},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.47449421882629395},{"id":"https://openalex.org/keywords/bittorrent-tracker","display_name":"BitTorrent tracker","score":0.46863460540771484},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4592457115650177},{"id":"https://openalex.org/keywords/affine-combination","display_name":"Affine combination","score":0.4519776701927185},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4150613844394684},{"id":"https://openalex.org/keywords/harris-affine-region-detector","display_name":"Harris affine region detector","score":0.4128718078136444},{"id":"https://openalex.org/keywords/affine-shape-adaptation","display_name":"Affine shape adaptation","score":0.3660033345222473},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.3020496368408203},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.2854883074760437},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.162203848361969}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8329979181289673},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.77195143699646},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.745446503162384},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7317658066749573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7272392511367798},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6065264344215393},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5777796506881714},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.47449421882629395},{"id":"https://openalex.org/C57501372","wikidata":"https://www.wikidata.org/wiki/Q2021268","display_name":"BitTorrent tracker","level":3,"score":0.46863460540771484},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4592457115650177},{"id":"https://openalex.org/C207221997","wikidata":"https://www.wikidata.org/wiki/Q938614","display_name":"Affine combination","level":3,"score":0.4519776701927185},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4150613844394684},{"id":"https://openalex.org/C6408098","wikidata":"https://www.wikidata.org/wiki/Q17021115","display_name":"Harris affine region detector","level":5,"score":0.4128718078136444},{"id":"https://openalex.org/C18516315","wikidata":"https://www.wikidata.org/wiki/Q4688950","display_name":"Affine shape adaptation","level":4,"score":0.3660033345222473},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.3020496368408203},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2854883074760437},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.162203848361969},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2960105","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2960105","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08933387.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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:568b4a979f62497b9614729c1837b529","is_oa":true,"landing_page_url":"https://doaj.org/article/568b4a979f62497b9614729c1837b529","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 182489-182498 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2960105","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2960105","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08933387.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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G6005532943","display_name":null,"funder_award_id":"61603415","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8943586476","display_name":null,"funder_award_id":"61603080","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G947955987","display_name":null,"funder_award_id":"61503274","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2995399338.pdf","grobid_xml":"https://content.openalex.org/works/W2995399338.grobid-xml"},"referenced_works_count":62,"referenced_works":["https://openalex.org/W29474918","https://openalex.org/W134197611","https://openalex.org/W161114242","https://openalex.org/W1551148884","https://openalex.org/W1686810756","https://openalex.org/W1811986364","https://openalex.org/W1915599933","https://openalex.org/W1955514522","https://openalex.org/W1955741794","https://openalex.org/W1964846093","https://openalex.org/W1995903777","https://openalex.org/W1997121481","https://openalex.org/W2024657922","https://openalex.org/W2044986361","https://openalex.org/W2054654728","https://openalex.org/W2060814785","https://openalex.org/W2070131823","https://openalex.org/W2075742732","https://openalex.org/W2087885569","https://openalex.org/W2089961441","https://openalex.org/W2094274531","https://openalex.org/W2096487710","https://openalex.org/W2098941887","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2109579504","https://openalex.org/W2118902001","https://openalex.org/W2124081409","https://openalex.org/W2124211486","https://openalex.org/W2126302311","https://openalex.org/W2139047213","https://openalex.org/W2149270732","https://openalex.org/W2154889144","https://openalex.org/W2158076914","https://openalex.org/W2158592639","https://openalex.org/W2161381512","https://openalex.org/W2163605009","https://openalex.org/W2165037244","https://openalex.org/W2214352687","https://openalex.org/W2283732452","https://openalex.org/W2463288413","https://openalex.org/W2497737031","https://openalex.org/W2545571035","https://openalex.org/W2550770188","https://openalex.org/W2740685955","https://openalex.org/W2767302379","https://openalex.org/W2805698795","https://openalex.org/W2897464893","https://openalex.org/W2916780012","https://openalex.org/W2917435394","https://openalex.org/W3102624093","https://openalex.org/W4205510932","https://openalex.org/W6601205835","https://openalex.org/W6605365747","https://openalex.org/W6606595081","https://openalex.org/W6637373629","https://openalex.org/W6649598916","https://openalex.org/W6664125481","https://openalex.org/W6665839387","https://openalex.org/W6668260035","https://openalex.org/W6684191040","https://openalex.org/W6684332838"],"related_works":["https://openalex.org/W2184784635","https://openalex.org/W2077614002","https://openalex.org/W2020310427","https://openalex.org/W2038416447","https://openalex.org/W2162196222","https://openalex.org/W1969252010","https://openalex.org/W1510464305","https://openalex.org/W2016861691","https://openalex.org/W2165644674","https://openalex.org/W2390890491"],"abstract_inverted_index":{"The":[0],"state-of-the-art":[1,137],"trackers":[2],"using":[3],"deep":[4],"learning":[5],"technology":[6],"have":[7],"no":[8],"special":[9],"strategy":[10],"to":[11,61,73,86,106,135],"capture":[12,27],"the":[13,17,22,28,34,68,88,94,108,127,136,140],"geometric":[14],"deformation":[15],"of":[16,37,47,77,110,130],"target.":[18],"Based":[19],"on":[20],"that":[21,33],"affine":[23,56,69],"manifold":[24],"can":[25,42],"better":[26,43],"target":[29,96],"shape":[30],"change":[31],"and":[32],"higher":[35],"level":[36],"Convolutional":[38],"Neural":[39],"Network":[40],"(CNN)":[41],"describe":[44],"semantic":[45],"information":[46],"objects,":[48],"we":[49],"propose":[50],"a":[51,78,81,99],"new":[52],"tracking":[53,132],"algorithm":[54,133],"combining":[55],"transformation":[57,70],"with":[58,64],"convolutional":[59,111],"features":[60],"track":[62],"targets":[63],"dramatic":[65],"deformation.":[66],"First,":[67],"is":[71,84,104,114],"applied":[72],"predict":[74],"possible":[75],"locations":[76],"target,":[79],"then":[80],"correlative":[82],"filter":[83,103],"designed":[85],"compute":[87],"appearance":[89],"confidence":[90],"score":[91],"for":[92,121],"determining":[93],"final":[95],"location.":[97],"Furthermore,":[98],"standard":[100],"discriminative":[101],"correlation":[102],"used":[105,120],"develop":[107],"effect":[109],"features,":[112],"which":[113],"more":[115],"efficient":[116],"than":[117],"other":[118],"methods":[119],"CNN":[122],"Networks.":[123],"Comprehensive":[124],"experiments":[125],"demonstrate":[126],"outstanding":[128],"performance":[129],"our":[131],"compared":[134],"techniques":[138],"in":[139],"public":[141],"benchmarks.":[142]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
