{"id":"https://openalex.org/W3047417463","doi":"https://doi.org/10.1109/iscc50000.2020.9219660","title":"An AI-based Visual Attention Model for Vehicle Make and Model Recognition","display_name":"An AI-based Visual Attention Model for Vehicle Make and Model Recognition","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3047417463","doi":"https://doi.org/10.1109/iscc50000.2020.9219660","mag":"3047417463"},"language":"en","primary_location":{"id":"doi:10.1109/iscc50000.2020.9219660","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscc50000.2020.9219660","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Symposium on Computers and Communications (ISCC)","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/A5086683628","display_name":"Xiren Ma","orcid":"https://orcid.org/0000-0002-5374-4543"},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Xiren Ma","raw_affiliation_strings":["PARADISE Research Laboratory, EECS, University of Ottawa, Canada"],"affiliations":[{"raw_affiliation_string":"PARADISE Research Laboratory, EECS, University of Ottawa, Canada","institution_ids":["https://openalex.org/I153718931"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012195474","display_name":"Azzedine Boukerche","orcid":"https://orcid.org/0000-0002-3851-9938"},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Azzedine Boukerche","raw_affiliation_strings":["PARADISE Research Laboratory, EECS, University of Ottawa, Canada"],"affiliations":[{"raw_affiliation_string":"PARADISE Research Laboratory, EECS, University of Ottawa, Canada","institution_ids":["https://openalex.org/I153718931"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5086683628"],"corresponding_institution_ids":["https://openalex.org/I153718931"],"apc_list":null,"apc_paid":null,"fwci":1.1724,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.81179001,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T10036","display_name":"Advanced Neural Network Applications","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":0.9983999729156494,"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":0.9979000091552734,"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":0.8097670078277588},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7469111680984497},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7296386957168579},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6983222365379333},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.5888117551803589},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5740249752998352},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.5281201601028442},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45118600130081177},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36839771270751953},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10785099864006042},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.09373757243156433}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8097670078277588},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7469111680984497},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7296386957168579},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6983222365379333},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.5888117551803589},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5740249752998352},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.5281201601028442},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45118600130081177},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36839771270751953},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10785099864006042},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.09373757243156433},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscc50000.2020.9219660","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscc50000.2020.9219660","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Symposium on Computers and Communications (ISCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.550000011920929,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1898560071","https://openalex.org/W1958236864","https://openalex.org/W2033391939","https://openalex.org/W2064675550","https://openalex.org/W2104657103","https://openalex.org/W2108598243","https://openalex.org/W2118615399","https://openalex.org/W2138011018","https://openalex.org/W2157331557","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2401231614","https://openalex.org/W2460852148","https://openalex.org/W2549125172","https://openalex.org/W2561238782","https://openalex.org/W2604702198","https://openalex.org/W2605117450","https://openalex.org/W2707890836","https://openalex.org/W2737725206","https://openalex.org/W2761909754","https://openalex.org/W2773003563","https://openalex.org/W2791779647","https://openalex.org/W2798365843","https://openalex.org/W2883805248","https://openalex.org/W2889968392","https://openalex.org/W2896137401","https://openalex.org/W2898041613","https://openalex.org/W2898131585","https://openalex.org/W2899771611","https://openalex.org/W2902869558","https://openalex.org/W2962835968","https://openalex.org/W2964137095","https://openalex.org/W2964341597","https://openalex.org/W3124951096","https://openalex.org/W6637373629","https://openalex.org/W6680138032","https://openalex.org/W6730179637","https://openalex.org/W6736321067","https://openalex.org/W6739917289","https://openalex.org/W6756040250"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2378211422","https://openalex.org/W2110523656","https://openalex.org/W3088304681"],"abstract_inverted_index":{"With":[0,45],"the":[1,41,46,49,53,58,71,77,81,89,109,124,139,160,194,201,208],"increasing":[2],"highlighted":[3],"security":[4],"concerns":[5],"in":[6,23,33,142],"Intelligent":[7],"Transportation":[8],"System":[9],"(ITS),":[10],"Vehicle":[11],"Make":[12],"and":[13,40,67,74,133,187,205],"Model":[14],"Recognition":[15],"(VMMR)":[16],"has":[17],"attracted":[18],"a":[19,101,128,136,143,148],"lot":[20],"of":[21,48,127,159,172,198],"attention":[22],"recent":[24],"years.":[25],"The":[26,118,170,190],"VMMR":[27,60,84,180],"method":[28,61],"can":[29,62,75,152],"be":[30,63,153],"widely":[31],"used":[32],"suspicious":[34,82],"vehicle":[35,54,94,129],"recognition,":[36],"urban":[37],"traffic":[38],"monitoring,":[39],"automated":[42],"driving":[43],"system.":[44],"development":[47],"Vehicle-to-Everything":[50],"(V2X)":[51],"technology,":[52],"information":[55,141],"recognized":[56],"by":[57],"AI-based":[59],"shared":[64],"among":[65,93],"vehicles":[66],"other":[68],"participants":[69],"within":[70],"transportation":[72],"system,":[73],"help":[76],"police":[78],"fast":[79],"locate":[80],"vehicles.":[83],"is":[85,147,175],"complicated":[86],"due":[87],"to":[88,107,122,156,164],"subtle":[90],"visual":[91],"differences":[92],"models.":[95],"In":[96],"this":[97],"paper,":[98],"we":[99],"propose":[100],"novel":[102],"Recurrent":[103],"Attention":[104],"Unit":[105],"(RAU)":[106],"expand":[108],"standard":[110],"Convolutional":[111],"Neural":[112],"Network":[113],"(CNN)":[114],"architecture":[115],"for":[116],"VMMR.":[117,169],"proposed":[119,191],"RAU":[120,146],"learns":[121],"recognize":[123],"discriminative":[125],"part":[126],"on":[130,168,177,200,207],"multiple":[131],"scales":[132],"builds":[134],"up":[135],"connection":[137],"with":[138],"prominent":[140],"recurrent":[144],"way.":[145],"modular":[149],"unit.":[150],"It":[151],"easily":[154],"applied":[155],"different":[157],"layers":[158],"vanilla":[161],"CNN":[162],"architectures":[163],"boost":[165],"their":[166],"performance":[167],"efficiency":[171],"our":[173],"models":[174],"tested":[176],"three":[178],"challenging":[179],"benchmark":[181],"datasets,":[182],"i.e.,":[183],"Stanford":[184,202],"Cars,":[185],"CompCars,":[186],"CompCars":[188,209],"Surveillance.":[189],"ResNet101-RAU":[192],"achieves":[193],"best":[195],"recognition":[196],"accuracy":[197],"93.81%":[199],"Cars":[203],"dataset":[204],"97.84%":[206],"dataset.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}