{"id":"https://openalex.org/W3158501847","doi":"https://doi.org/10.1145/3412841.3442072","title":"Quantitative comparison of supervised algorithms and feature sets for traffic sign recognition","display_name":"Quantitative comparison of supervised algorithms and feature sets for traffic sign recognition","publication_year":2021,"publication_date":"2021-03-22","ids":{"openalex":"https://openalex.org/W3158501847","doi":"https://doi.org/10.1145/3412841.3442072","mag":"3158501847"},"language":"en","primary_location":{"id":"doi:10.1145/3412841.3442072","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412841.3442072","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","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/A5005797686","display_name":"Muhammad Atif","orcid":"https://orcid.org/0000-0002-3460-7164"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Muhammad Atif","raw_affiliation_strings":["University of Florence, Florence, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florence, Florence, Italy","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027443752","display_name":"Tommaso Zoppi","orcid":"https://orcid.org/0000-0001-9820-6047"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Tommaso Zoppi","raw_affiliation_strings":["University of Florence, Florence, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florence, Florence, Italy","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081356359","display_name":"Mohamad Gharib","orcid":"https://orcid.org/0000-0003-2286-2819"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Mohamad Gharib","raw_affiliation_strings":["University of Florence, Florence, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florence, Florence, Italy","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016669256","display_name":"Andrea Bondavalli","orcid":"https://orcid.org/0000-0001-7366-6530"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Andrea Bondavalli","raw_affiliation_strings":["University of Florence, Florence, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florence, Florence, Italy","institution_ids":["https://openalex.org/I45084792"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4851,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.63997355,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"174","last_page":"177"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9988999962806702,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9988999962806702,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9984999895095825,"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/computer-science","display_name":"Computer science","score":0.7752598524093628},{"id":"https://openalex.org/keywords/traffic-sign-recognition","display_name":"Traffic sign recognition","score":0.766372561454773},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7027125954627991},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6379998326301575},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5820774435997009},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5751709342002869},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5549366474151611},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5224589109420776},{"id":"https://openalex.org/keywords/traffic-sign","display_name":"Traffic sign","score":0.518117368221283},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3877646028995514},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3390401005744934},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3365756869316101},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.2711133360862732},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07111722230911255}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7752598524093628},{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.766372561454773},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7027125954627991},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6379998326301575},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5820774435997009},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5751709342002869},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5549366474151611},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5224589109420776},{"id":"https://openalex.org/C2983860417","wikidata":"https://www.wikidata.org/wiki/Q170285","display_name":"Traffic sign","level":3,"score":0.518117368221283},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3877646028995514},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3390401005744934},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3365756869316101},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.2711133360862732},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07111722230911255},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3412841.3442072","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412841.3442072","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:flore.unifi.it:2158/1238468","is_oa":false,"landing_page_url":"http://hdl.handle.net/2158/1238468","pdf_url":null,"source":{"id":"https://openalex.org/S4306402033","display_name":"Florence Research (University of Florence)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45084792","host_organization_name":"University of Florence","host_organization_lineage":["https://openalex.org/I45084792"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.6499999761581421,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1975509163","https://openalex.org/W2016397958","https://openalex.org/W2067713319","https://openalex.org/W2537632740","https://openalex.org/W2587895181","https://openalex.org/W2753678564","https://openalex.org/W2783076614","https://openalex.org/W2887801798","https://openalex.org/W2897451483","https://openalex.org/W2964682716","https://openalex.org/W2971714251","https://openalex.org/W3132476814","https://openalex.org/W6757843174","https://openalex.org/W6771392127","https://openalex.org/W6893712974","https://openalex.org/W7043910698","https://openalex.org/W7064945401"],"related_works":["https://openalex.org/W4382897155","https://openalex.org/W4283820116","https://openalex.org/W4379231512","https://openalex.org/W4378699879","https://openalex.org/W3128164723","https://openalex.org/W4286647459","https://openalex.org/W2899819381","https://openalex.org/W2557202782","https://openalex.org/W3215426395","https://openalex.org/W2772251146"],"abstract_inverted_index":{"Nowadays":[0],"a":[1,17,96,119],"timely":[2],"detection":[3,44],"of":[4,11,47,65,101,123,140],"relevant":[5],"events":[6],"and":[7,45,54,71,109,133,166],"an":[8,14],"efficient":[9],"recognition":[10,46,139],"objects":[12],"in":[13],"environment":[15,35],"is":[16,55],"critical":[18],"activity":[19],"for":[20,137],"many":[21,69,154],"Cyber-Physical":[22],"Systems":[23],"(CPS),":[24],"which":[25],"may":[26],"have":[27,84],"severe":[28],"impact":[29],"on":[30,82,106,157],"citizens,":[31],"infrastructures":[32],"or":[33],"the":[34,40,43,63,88,102,138,158,164],"when":[36],"incurring":[37],"malfunctions.":[38],"In":[39],"automotive":[41],"domain,":[42],"traffic":[48,141],"signs":[49,142],"(TSDR)":[50],"from":[51,143],"images":[52,77],"was":[53,151],"currently":[56],"being":[57],"investigated":[58],"as":[59],"it":[60],"heavily":[61],"impacts":[62],"behavior":[64],"(semi-)autonomous":[66],"vehicles.":[67],"Despite":[68],"classifiers":[70],"feature":[72,110,131],"extraction":[73],"strategies":[74],"applied":[75],"to":[76],"sampled":[78],"by":[79,117,153],"webcams":[80],"installed":[81],"cars":[83],"been":[85],"developed":[86],"throughout":[87],"years,":[89],"those":[90],"efforts":[91],"did":[92],"not":[93],"escalate":[94],"into":[95],"clear":[97],"benchmark":[98],"nor":[99],"comparison":[100,122],"most":[103],"common":[104],"techniques":[105],"multiple":[107],"datasets":[108,168],"sets.":[111],"This":[112],"study":[113],"tackles":[114],"this":[115],"problem":[116],"providing":[118],"comprehensive":[120],"quantitative":[121],"traditional":[124],"supervised":[125],"Machine":[126],"Learning":[127,135],"algorithms":[128,156,170],"with":[129,163],"different":[130],"sets":[132],"Deep":[134],"models":[136],"three":[144],"publicly":[145],"available":[146],"datasets.":[147],"A":[148],"perfect":[149,172],"classification":[150],"achieved":[152],"ML":[155],"German":[159],"GTRSB":[160],"dataset,":[161],"while":[162],"BelgiumTSC":[165],"DITS":[167],"no":[169],"provided":[171],"classification.":[173]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}