{"id":"https://openalex.org/W2978400640","doi":"https://doi.org/10.1109/ijcnn.2019.8851940","title":"Correlation Filter Tracking Method via Metric Learning and Adaptive Multi-stage Appearance","display_name":"Correlation Filter Tracking Method via Metric Learning and Adaptive Multi-stage Appearance","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978400640","doi":"https://doi.org/10.1109/ijcnn.2019.8851940","mag":"2978400640"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851940","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851940","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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":null,"display_name":"Yan Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yan Hong","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100337001","display_name":"Jing Li","orcid":"https://orcid.org/0000-0002-8181-0886"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Li","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067224801","display_name":"Yafu Xiao","orcid":"https://orcid.org/0000-0001-5164-9983"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yafu Xiao","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068146495","display_name":"Wenfan Zhang","orcid":"https://orcid.org/0000-0002-6084-3574"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenfan Zhang","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004712938","display_name":"Chengfang Song","orcid":"https://orcid.org/0000-0002-2002-6115"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengfang Song","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101912402","display_name":"Shan Xue","orcid":"https://orcid.org/0000-0002-9123-5133"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shan Xue","raw_affiliation_strings":["Department of Computing, Macquarie University, Sydney, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Macquarie University, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I99043593"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10625788,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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/T11963","display_name":"Impact of Light on Environment and Health","score":0.9380000233650208,"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"}},{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9315000176429749,"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/clutter","display_name":"Clutter","score":0.843686580657959},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6637630462646484},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6367215514183044},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6033746004104614},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5584288239479065},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5466012954711914},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.533768892288208},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.4927678406238556},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41483935713768005},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38132238388061523},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3554656505584717},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.15051934123039246},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.12075546383857727}],"concepts":[{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.843686580657959},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6637630462646484},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6367215514183044},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6033746004104614},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5584288239479065},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5466012954711914},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.533768892288208},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.4927678406238556},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41483935713768005},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38132238388061523},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3554656505584717},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.15051934123039246},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12075546383857727},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851940","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851940","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W182940129","https://openalex.org/W818325216","https://openalex.org/W1528914981","https://openalex.org/W1857884451","https://openalex.org/W1955514522","https://openalex.org/W1955741794","https://openalex.org/W1964846093","https://openalex.org/W1997121481","https://openalex.org/W2037034832","https://openalex.org/W2140340527","https://openalex.org/W2140595412","https://openalex.org/W2154889144","https://openalex.org/W2158592639","https://openalex.org/W2162349892","https://openalex.org/W2164278908","https://openalex.org/W2165037244","https://openalex.org/W2167089254","https://openalex.org/W2168356304","https://openalex.org/W2169495281","https://openalex.org/W2214352687","https://openalex.org/W2244956674","https://openalex.org/W2262355565","https://openalex.org/W2343187456","https://openalex.org/W2346421499","https://openalex.org/W2469582947","https://openalex.org/W2470394683","https://openalex.org/W2473868734","https://openalex.org/W2526426687","https://openalex.org/W2550919154","https://openalex.org/W2557641257","https://openalex.org/W2558899534","https://openalex.org/W2605381261","https://openalex.org/W2681067697","https://openalex.org/W2683203202","https://openalex.org/W2735864029","https://openalex.org/W2742165450","https://openalex.org/W2754938303","https://openalex.org/W2776035257","https://openalex.org/W2888819616","https://openalex.org/W2896121375","https://openalex.org/W2896959664","https://openalex.org/W2917435394","https://openalex.org/W2962824803","https://openalex.org/W2962972233","https://openalex.org/W2964069521","https://openalex.org/W2964111344","https://openalex.org/W2964198573","https://openalex.org/W3102624093","https://openalex.org/W4292363360","https://openalex.org/W6720898849","https://openalex.org/W6730584787","https://openalex.org/W6736018854","https://openalex.org/W6745108999"],"related_works":["https://openalex.org/W2130674020","https://openalex.org/W2093748878","https://openalex.org/W2333771223","https://openalex.org/W2120056845","https://openalex.org/W1981531423","https://openalex.org/W4394861761","https://openalex.org/W2154140103","https://openalex.org/W3140584830","https://openalex.org/W1989793085","https://openalex.org/W2215635302"],"abstract_inverted_index":{"In":[0],"the":[1,15,19,27,31,35,41,51,93,96,114,122,134,137,144,147,161,167,182],"complex":[2,106],"tracking":[3,71,103,195],"environment":[4],"such":[5,108],"as":[6,109],"background":[7,110],"clutter,":[8],"generally":[9],"there":[10],"are":[11],"multiple":[12,90],"peaks":[13,91],"in":[14,92,105,169],"response":[16,94,100],"map":[17,101],"of":[18,98,146],"correlation":[20,69],"filter.":[21],"It":[22],"is":[23,45,112,118,140,172,179,187,190],"difficult":[24,48],"to":[25,39,49,57,84,89,120,133],"distinguish":[26],"real":[28],"object":[29,123],"from":[30],"interference;":[32],"and":[33,76,128,136,156,181],"using":[34],"fixed":[36],"learning":[37,75,83],"rate":[38],"update":[40],"appearance":[42,124,148],"model,":[43],"it":[44],"not":[46],"only":[47],"maintain":[50],"sample":[52],"diversity,":[53],"but":[54],"also":[55],"easy":[56],"introduce":[58],"noise":[59],"information.":[60],"Aiming":[61],"at":[62],"this":[63,65,170],"problem,":[64],"paper":[66,171],"proposes":[67],"a":[68],"filter":[70],"method":[72],"via":[73],"metric":[74,82],"adaptive":[77],"multi-stage":[78],"appearance.":[79],"By":[80],"introducing":[81],"discriminate":[85],"candidate":[86],"samples":[87,125],"corresponding":[88,130],"map,":[95],"influence":[97],"multi-peak":[99],"on":[102,154],"results":[104,153],"environments":[107],"clutter":[111],"eliminated;":[113],"Gaussian":[115],"mixture":[116],"model":[117,149],"used":[119],"divide":[121],"into":[126],"groups":[127],"assign":[129],"weights":[131],"according":[132],"duration,":[135],"redundant":[138],"information":[139],"eliminated":[141],"while":[142],"maintaining":[143],"diversity":[145],"samples.":[150],"The":[151,174],"experimental":[152],"OTB100":[155],"VOT2017":[157],"datasets":[158],"show":[159],"that":[160],"overall":[162,175],"precision":[163],"score":[164,178,186],"obtained":[165],"by":[166],"algorithm":[168],"0.866.":[173],"success":[176],"plot":[177],"0.628,":[180],"expected":[183],"average":[184],"overlap":[185],"0.211,":[188],"which":[189],"better":[191],"than":[192],"most":[193],"existing":[194],"methods.":[196]},"counts_by_year":[],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
