{"id":"https://openalex.org/W2808286444","doi":"https://doi.org/10.24963/ijcai.2018/108","title":"Feature Integration with Adaptive Importance Maps for Visual Tracking","display_name":"Feature Integration with Adaptive Importance Maps for Visual Tracking","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2808286444","doi":"https://doi.org/10.24963/ijcai.2018/108","mag":"2808286444"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2018/108","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/108","pdf_url":"https://www.ijcai.org/proceedings/2018/0108.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2018/0108.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070454737","display_name":"Aishi Li","orcid":null},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aishi Li","raw_affiliation_strings":["Nanjing Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing Normal University","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101400128","display_name":"Ming Yang","orcid":"https://orcid.org/0000-0001-8936-4270"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Yang","raw_affiliation_strings":["Nanjing Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing Normal University","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078658353","display_name":"Wanqi Yang","orcid":"https://orcid.org/0000-0001-6727-6077"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanqi Yang","raw_affiliation_strings":["Nanjing Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing Normal University","institution_ids":["https://openalex.org/I152031979"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I152031979"],"apc_list":null,"apc_paid":null,"fwci":0.1521,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.48812645,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"779","last_page":"785"},"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":0.9801999926567078,"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":0.9761999845504761,"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/computer-science","display_name":"Computer science","score":0.755882740020752},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.703064501285553},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.679271399974823},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6618453860282898},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6390929222106934},{"id":"https://openalex.org/keywords/bittorrent-tracker","display_name":"BitTorrent tracker","score":0.5942116379737854},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5269477963447571},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5090320110321045},{"id":"https://openalex.org/keywords/data-association","display_name":"Data association","score":0.4308621287345886},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4245207905769348},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3867989778518677},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.3692144751548767},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14941763877868652}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.755882740020752},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.703064501285553},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.679271399974823},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6618453860282898},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6390929222106934},{"id":"https://openalex.org/C57501372","wikidata":"https://www.wikidata.org/wiki/Q2021268","display_name":"BitTorrent tracker","level":3,"score":0.5942116379737854},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5269477963447571},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5090320110321045},{"id":"https://openalex.org/C2983325608","wikidata":"https://www.wikidata.org/wiki/Q17084606","display_name":"Data association","level":3,"score":0.4308621287345886},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4245207905769348},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3867989778518677},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.3692144751548767},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14941763877868652},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2018/108","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/108","pdf_url":"https://www.ijcai.org/proceedings/2018/0108.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2018/108","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/108","pdf_url":"https://www.ijcai.org/proceedings/2018/0108.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7699999809265137,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2808286444.pdf","grobid_xml":"https://content.openalex.org/works/W2808286444.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W161114242","https://openalex.org/W818325216","https://openalex.org/W1892578678","https://openalex.org/W1904671147","https://openalex.org/W1964846093","https://openalex.org/W2089961441","https://openalex.org/W2118345389","https://openalex.org/W2154889144","https://openalex.org/W2211807644","https://openalex.org/W2470394683","https://openalex.org/W2518013266","https://openalex.org/W2557641257","https://openalex.org/W2558899534","https://openalex.org/W2597397952","https://openalex.org/W2599547527","https://openalex.org/W2681067697","https://openalex.org/W2768634781","https://openalex.org/W6736566681","https://openalex.org/W6791858558","https://openalex.org/W6863631769","https://openalex.org/W6864014924"],"related_works":["https://openalex.org/W3040691452","https://openalex.org/W2803618243","https://openalex.org/W256589335","https://openalex.org/W3020706491","https://openalex.org/W2012200063","https://openalex.org/W4385454113","https://openalex.org/W2804764393","https://openalex.org/W4386114301","https://openalex.org/W4386158955","https://openalex.org/W4384788979"],"abstract_inverted_index":{"Discriminative":[0],"correlation":[1,62],"filters":[2],"have":[3],"recently":[4],"achieved":[5],"excellent":[6],"performance":[7,145],"for":[8,61,94,116],"visual":[9],"object":[10],"tracking.":[11],"The":[12],"key":[13],"to":[14,17,86,106,125],"success":[15],"is":[16,100],"make":[18],"full":[19],"use":[20,121],"of":[21,27,46,58,82,129],"dense":[22],"sampling":[23],"and":[24,43,90,120,134],"specific":[25],"properties":[26],"circulant":[28],"matrices":[29],"in":[30,73],"the":[31,41,80,117,122,127],"Fourier":[32],"domain.":[33],"However,":[34],"previous":[35],"studies":[36],"don't":[37],"take":[38],"into":[39],"consideration":[40],"importance":[42,71,77,98,118],"complementary":[44,88],"information":[45],"different":[47,83],"features,":[48,84,136],"simply":[49],"concatenating":[50],"them.":[51],"This":[52],"paper":[53],"investigates":[54],"an":[55,97],"effective":[56],"method":[57],"feature":[59],"integration":[60],"filters,":[63,67],"which":[64,141],"jointly":[65],"learns":[66],"as":[68,70],"well":[69],"maps":[72,78,119],"each":[74,95],"frame.":[75],"These":[76],"borrow":[79],"advantages":[81],"aiming":[85],"achieve":[87,142],"traits":[89],"improve":[91],"robustness.":[92],"Moreover,":[93],"feature,":[96],"map":[99],"shared":[101],"by":[102],"its":[103],"all":[104],"channels":[105],"avoid":[107],"overfitting.":[108],"In":[109],"addition,":[110],"we":[111,137],"introduce":[112],"a":[113,143],"regularization":[114],"term":[115],"penalty":[123],"factor":[124],"control":[126],"significance":[128],"features.":[130],"Based":[131],"on":[132],"handcrafted":[133],"CNN":[135],"implement":[138],"two":[139],"trackers,":[140],"competitive":[144],"compared":[146],"with":[147],"several":[148],"state-of-the-art":[149],"trackers.":[150]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}