{"id":"https://openalex.org/W3116192094","doi":"https://doi.org/10.1109/itsc45102.2020.9294672","title":"A Lightweight Deep Learning Model for Vehicle Viewpoint Estimation from Dashcam Images","display_name":"A Lightweight Deep Learning Model for Vehicle Viewpoint Estimation from Dashcam Images","publication_year":2020,"publication_date":"2020-09-20","ids":{"openalex":"https://openalex.org/W3116192094","doi":"https://doi.org/10.1109/itsc45102.2020.9294672","mag":"3116192094"},"language":"en","primary_location":{"id":"doi:10.1109/itsc45102.2020.9294672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294672","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","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/A5056168149","display_name":"Simone Magistri","orcid":"https://orcid.org/0000-0002-0520-8463"},"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":true,"raw_author_name":"Simone Magistri","raw_affiliation_strings":["University of Florence, Italy"],"affiliations":[{"raw_affiliation_string":"University of Florence, Italy","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017560687","display_name":"Francesco Sambo","orcid":"https://orcid.org/0000-0002-7726-5811"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Francesco Sambo","raw_affiliation_strings":["Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069745642","display_name":"Fabio Schoen","orcid":"https://orcid.org/0000-0003-1160-7572"},"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":"Fabio Schoen","raw_affiliation_strings":["University of Florence, Italy"],"affiliations":[{"raw_affiliation_string":"University of Florence, Italy","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030990358","display_name":"Douglas Coimbra de Andrade","orcid":"https://orcid.org/0000-0001-8981-143X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Douglas Coimbra de Andrade","raw_affiliation_strings":["Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087236050","display_name":"Matteo Simoncini","orcid":"https://orcid.org/0000-0001-7128-1792"},"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":"Matteo Simoncini","raw_affiliation_strings":["University of Florence, Italy","Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"University of Florence, Italy","institution_ids":["https://openalex.org/I45084792"]},{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071204593","display_name":"Stefano Caprasecca","orcid":"https://orcid.org/0000-0001-8058-0620"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stefano Caprasecca","raw_affiliation_strings":["Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054861755","display_name":"Luca Kubin","orcid":"https://orcid.org/0000-0002-1017-646X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luca Kubin","raw_affiliation_strings":["Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103133960","display_name":"Luca Bravi","orcid":"https://orcid.org/0000-0003-0899-6976"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luca Bravi","raw_affiliation_strings":["Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058185763","display_name":"Leonardo Taccari","orcid":"https://orcid.org/0000-0003-0800-4893"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leonardo Taccari","raw_affiliation_strings":["Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5056168149"],"corresponding_institution_ids":["https://openalex.org/I45084792"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.42984137,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T10036","display_name":"Advanced Neural Network Applications","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/T10331","display_name":"Video Surveillance and Tracking Methods","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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9983999729156494,"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":0.7919084429740906},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7374235987663269},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6314025521278381},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.6143366098403931},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5970869660377502},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5566441416740417},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48142072558403015},{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.48030799627304077},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4644835889339447},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.44551798701286316},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4383545517921448},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4373724162578583},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.423380047082901},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.41295409202575684},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12782227993011475}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7919084429740906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7374235987663269},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6314025521278381},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.6143366098403931},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5970869660377502},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5566441416740417},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48142072558403015},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.48030799627304077},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4644835889339447},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.44551798701286316},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4383545517921448},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4373724162578583},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.423380047082901},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.41295409202575684},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12782227993011475},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/itsc45102.2020.9294672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294672","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},{"id":"pmh:oai:flore.unifi.it:2158/1223142","is_oa":false,"landing_page_url":"https://ieeexplore.ieee.org/document/9294672","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/11","score":0.5099999904632568,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W112153112","https://openalex.org/W1522301498","https://openalex.org/W1591870335","https://openalex.org/W1686810756","https://openalex.org/W1949483711","https://openalex.org/W1991264156","https://openalex.org/W2008839571","https://openalex.org/W2031489346","https://openalex.org/W2108598243","https://openalex.org/W2144195102","https://openalex.org/W2151992422","https://openalex.org/W2194775991","https://openalex.org/W2518803647","https://openalex.org/W2784342561","https://openalex.org/W2796347433","https://openalex.org/W2802519414","https://openalex.org/W2897124911","https://openalex.org/W2899771611","https://openalex.org/W2903820641","https://openalex.org/W2904840670","https://openalex.org/W2904904022","https://openalex.org/W2905076052","https://openalex.org/W2952580332","https://openalex.org/W2963163009","https://openalex.org/W2964121744","https://openalex.org/W2966475089","https://openalex.org/W2968296999","https://openalex.org/W2969987486","https://openalex.org/W2981441441","https://openalex.org/W3035574168","https://openalex.org/W4293495027","https://openalex.org/W4293584584","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6750227808","https://openalex.org/W6756040250","https://openalex.org/W6760782946","https://openalex.org/W6767379092"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W2964084369","https://openalex.org/W3137434606","https://openalex.org/W4372263373","https://openalex.org/W2972711445"],"abstract_inverted_index":{"Vehicle":[0],"viewpoint":[1,26,47,52,92,116],"estimation":[2,48,53,93],"from":[3,27],"vehicle":[4,25,51,99,115],"cameras":[5],"is":[6,105],"a":[7,19,28,58],"crucial":[8],"component":[9],"of":[10,111,120],"road":[11],"scene":[12],"understanding.":[13],"In":[14],"this":[15,34],"paper,":[16],"we":[17,36,56,82],"propose":[18],"deep":[20,41],"lightweight":[21,79],"method":[22,104],"to":[23,49,70,107],"predict":[24],"single":[29],"RGB":[30],"dashcam":[31],"image.":[32],"To":[33,75],"aim,":[35],"customize":[37],"and":[38,80,96,123],"adapt":[39],"state-of-the-art":[40],"learning":[42],"techniques":[43],"for":[44],"general":[45],"object":[46],"the":[50,77,109,112],"task.":[54],"Furthermore,":[55],"define":[57],"novel":[59],"objective":[60],"function":[61],"that":[62],"takes":[63],"into":[64],"account":[65],"errors":[66],"at":[67],"different":[68],"granularity":[69],"improve":[71],"neural":[72],"network":[73],"training.":[74],"keep":[76],"model":[78],"fast,":[81],"rely":[83],"upon":[84],"MobileNetV2":[85],"as":[86],"backbone.":[87],"Tested":[88],"both":[89,121],"on":[90,97],"benchmark":[91],"data":[94,101],"(Pascal3D+)":[95],"actual":[98],"camera":[100],"(nuScenes),":[102],"our":[103],"shown":[106],"outperform":[108],"state":[110],"art":[113],"in":[114,118],"estimation,":[117],"terms":[119],"accuracy":[122],"memory":[124],"footprint.":[125]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
