{"id":"https://openalex.org/W3088234943","doi":"https://doi.org/10.2352/issn.2470-1173.2020.6.iriacv-052","title":"Perceptual License Plate Super-Resolution with CTC Loss","display_name":"Perceptual License Plate Super-Resolution with CTC Loss","publication_year":2020,"publication_date":"2020-01-26","ids":{"openalex":"https://openalex.org/W3088234943","doi":"https://doi.org/10.2352/issn.2470-1173.2020.6.iriacv-052","mag":"3088234943"},"language":"en","primary_location":{"id":"doi:10.2352/issn.2470-1173.2020.6.iriacv-052","is_oa":false,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2020.6.iriacv-052","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-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/A5040363021","display_name":"Zuzana B\u0131\u0301lkov\u00e1","orcid":"https://orcid.org/0000-0002-4516-6659"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zuzana B\u00edlkov\u00e1","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010934768","display_name":"Michal Hradi\u0161","orcid":"https://orcid.org/0000-0002-6364-129X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michal Hradi\u0161","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2038,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.55184823,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"32","issue":"6","first_page":"52","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9675999879837036,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9573000073432922,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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":0.7734204530715942},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6119809150695801},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.571446418762207},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5675228238105774},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5462063550949097},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5329609513282776},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5016999244689941},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4847071170806885},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4781242907047272},{"id":"https://openalex.org/keywords/fuel-efficiency","display_name":"Fuel efficiency","score":0.46512341499328613},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4538489580154419},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3420620560646057},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13720813393592834},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.1012820303440094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7734204530715942},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6119809150695801},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.571446418762207},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5675228238105774},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5462063550949097},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5329609513282776},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5016999244689941},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4847071170806885},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4781242907047272},{"id":"https://openalex.org/C45882903","wikidata":"https://www.wikidata.org/wiki/Q5042317","display_name":"Fuel efficiency","level":2,"score":0.46512341499328613},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4538489580154419},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3420620560646057},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13720813393592834},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.1012820303440094},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2352/issn.2470-1173.2020.6.iriacv-052","is_oa":false,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2020.6.iriacv-052","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2348909947","https://openalex.org/W4292672442","https://openalex.org/W3166204570","https://openalex.org/W3121246613","https://openalex.org/W2362101859","https://openalex.org/W2941610985","https://openalex.org/W2132137594","https://openalex.org/W2791431590","https://openalex.org/W2798482732","https://openalex.org/W1978900583"],"abstract_inverted_index":{"In":[0],"this":[1,25],"work,":[2],"we":[3,155],"explore":[4],"the":[5,49,93,157,160,170,182,185],"ability":[6],"to":[7,27,45],"estimate":[8],"vehicle":[9,64,89,109,125,199],"fuel":[10,40,102,193],"consumption":[11,103],"using":[12,129,162,169],"imagery":[13],"from":[14,66,201],"overhead":[15,72],"fisheye":[16,73,203],"lens":[17,74,204],"cameras":[18],"deployed":[19],"as":[20],"traffic":[21,33,75,167,189],"sensors.":[22],"We":[23,47,91,138],"utilize":[24],"information":[26],"simulate":[28],"vision-based":[29],"control":[30],"of":[31,38,57,61,101,124,133,151,159,174,184],"a":[32,36,58,67,121,130,146,166],"intersection,":[34],"with":[35,42,113,142],"goal":[37],"improving":[39],"economy":[41],"minimal":[43],"impact":[44],"mobility.":[46],"introduce":[48],"ORNL":[50],"Overhead":[51],"Vehicle":[52],"Data":[53],"set":[54,60,79,95,132,150],"(OOVD),":[55],"consisting":[56],"data":[59,78,94,149],"paired,":[62],"labeled":[63],"images":[65],"ground-based":[68],"camera":[69],"and":[70,117,127],"an":[71],"camera.":[76],"The":[77],"includes":[80],"segmentation":[81,106],"masks":[82],"based":[83,104,144,195],"on":[84,105,120,145,197],"Gaussian":[85],"mixture":[86],"models":[87,128],"for":[88,111,187,191],"detection.":[90],"show":[92,156],"utility":[96,158],"through":[97],"three":[98],"applications:":[99],"estimation":[100],"bounding":[107,115],"boxes,":[108,116],"discrimination":[110],"vehicles":[112],"large":[114,147],"fine-grained":[118],"classification":[119],"limited":[122],"number":[123],"makes":[126],"pre-trained":[131],"convolutional":[134],"neural":[135],"network":[136],"models.":[137],"compare":[139],"these":[140],"results":[141,180],"estimates":[143,200],"open-source":[148],"web-scraped":[152],"imagery.":[153],"Finally,":[154],"approach":[161,186],"reinforcement":[163],"learning":[164],"in":[165],"simulator":[168],"open":[171],"source":[172],"Simulation":[173],"Urban":[175],"Mobility":[176],"(SUMO)":[177],"package.":[178],"Our":[179],"demonstrate":[181],"feasibility":[183],"controlling":[188],"lights":[190],"better":[192],"efficiency":[194],"solely":[196],"visual":[198],"commercial,":[202],"cameras.":[205]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
