{"id":"https://openalex.org/W3090202255","doi":"https://doi.org/10.1109/icip40778.2020.9191029","title":"An Effective Hierarchical Resolution Learning Method for Low-Resolution Targets Tracking","display_name":"An Effective Hierarchical Resolution Learning Method for Low-Resolution Targets Tracking","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3090202255","doi":"https://doi.org/10.1109/icip40778.2020.9191029","mag":"3090202255"},"language":"en","primary_location":{"id":"doi:10.1109/icip40778.2020.9191029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9191029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","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/A5084505306","display_name":"Runqing Zhang","orcid":"https://orcid.org/0000-0002-0503-7254"},"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":"Runqing Zhang","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100780601","display_name":"Chunxiao Fan","orcid":"https://orcid.org/0000-0002-3607-4904"},"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":"Chunxiao Fan","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089582342","display_name":"Yue Ming","orcid":"https://orcid.org/0000-0001-7105-4207"},"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":"Yue Ming","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101611864","display_name":"Hao Fu","orcid":"https://orcid.org/0000-0003-3755-8756"},"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":"Hao Fu","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019032012","display_name":"Xuyang Meng","orcid":"https://orcid.org/0000-0003-2379-2832"},"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":"Xuyang Meng","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10580837,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2076","last_page":"2080"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9997000098228455,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9997000098228455,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9995999932289124,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9994999766349792,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7885336875915527},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.73662930727005},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6977531313896179},{"id":"https://openalex.org/keywords/bittorrent-tracker","display_name":"BitTorrent tracker","score":0.6966149806976318},{"id":"https://openalex.org/keywords/low-resolution","display_name":"Low resolution","score":0.6771936416625977},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.658953070640564},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.590461015701294},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.5633702278137207},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45125094056129456},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.41882622241973877},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.3253386318683624},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.2960560619831085},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06455239653587341},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.060927003622055054}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7885336875915527},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.73662930727005},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6977531313896179},{"id":"https://openalex.org/C57501372","wikidata":"https://www.wikidata.org/wiki/Q2021268","display_name":"BitTorrent tracker","level":3,"score":0.6966149806976318},{"id":"https://openalex.org/C3019883945","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Low resolution","level":3,"score":0.6771936416625977},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.658953070640564},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.590461015701294},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5633702278137207},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45125094056129456},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.41882622241973877},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.3253386318683624},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.2960560619831085},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06455239653587341},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.060927003622055054},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip40778.2020.9191029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9191029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W2154889144","https://openalex.org/W2154924589","https://openalex.org/W2158592639","https://openalex.org/W2242218935","https://openalex.org/W2308101195","https://openalex.org/W2518876086","https://openalex.org/W2557641257","https://openalex.org/W2747898905","https://openalex.org/W2784746431","https://openalex.org/W2889607444","https://openalex.org/W2922262864","https://openalex.org/W2927438889","https://openalex.org/W2955983623","https://openalex.org/W2963030525","https://openalex.org/W2963182372","https://openalex.org/W2963774720","https://openalex.org/W2987346479","https://openalex.org/W2988310840","https://openalex.org/W6639309558","https://openalex.org/W6690498163","https://openalex.org/W6726654379","https://openalex.org/W6744992241"],"related_works":["https://openalex.org/W251433779","https://openalex.org/W2362774332","https://openalex.org/W2037893049","https://openalex.org/W4249245269","https://openalex.org/W2025681766","https://openalex.org/W2765548132","https://openalex.org/W2159897444","https://openalex.org/W2542402767","https://openalex.org/W4212954839","https://openalex.org/W4401570279"],"abstract_inverted_index":{"Suffering":[0],"from":[1,39,122],"the":[2,7,45,48,53,66,70,75,82,87,91,96,101,104],"low-resolution":[3,25,78],"target's":[4,54],"visual":[5,10],"quality,":[6],"precisions":[8],"of":[9,86,95,103],"object":[11],"trackers":[12,131],"are":[13],"reduced.":[14],"This":[15],"paper":[16],"proposes":[17],"an":[18],"effective":[19],"hierarchical":[20,34],"resolution":[21,41],"learning":[22],"method":[23],"for":[24],"targets":[26],"tracking,":[27],"abbreviated":[28],"as":[29],"HRT.":[30],"We":[31],"adopt":[32],"a":[33],"structure":[35,84],"to":[36,69],"exploit":[37],"information":[38,85],"different":[40],"levels.":[42],"(1)":[43],"At":[44,74],"high":[46],"level:":[47,77],"super-resolution":[49],"(SR)":[50],"images,":[51],"determining":[52],"shape,":[55],"contains":[56],"richer":[57],"image":[58],"textures":[59],"and":[60,64,116],"clearer":[61],"target":[62],"contours,":[63],"transmits":[65],"search":[67],"region":[68],"low":[71,76],"level.":[72],"(2)":[73],"(LR)":[79],"images":[80],"maintain":[81],"spatial":[83],"original":[88],"target,":[89],"providing":[90],"precise":[92],"center":[93],"coordinates":[94],"target.":[97],"Experimental":[98],"results":[99],"demonstrate":[100],"effectiveness":[102],"proposed":[105],"tracker,":[106],"which":[107],"HRT":[108],"achieves":[109],"90.3%":[110],"precision":[111,118],"on":[112,119],"OTB100":[113],"LR":[114,120],"sequences":[115,121],"78.5%":[117],"UAV123":[123],"datasets,":[124],"gaining":[125],"2.0%,":[126],"2.4%":[127],"improvement":[128],"over":[129],"state-of-the-art":[130],"respectively.":[132]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}