{"id":"https://openalex.org/W3006268246","doi":"https://doi.org/10.1109/icce-tw46550.2019.8991708","title":"Adaptive Vehicle Detection and Classification Scheme for Urban Traffic Scenes Using Convolutional Neural Network","display_name":"Adaptive Vehicle Detection and Classification Scheme for Urban Traffic Scenes Using Convolutional Neural Network","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W3006268246","doi":"https://doi.org/10.1109/icce-tw46550.2019.8991708","mag":"3006268246"},"language":"en","primary_location":{"id":"doi:10.1109/icce-tw46550.2019.8991708","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-tw46550.2019.8991708","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5050424046","display_name":"Yang Dao-Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dao-Wei Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102784814","display_name":"Hsin\u2010Tzu Wang","orcid":"https://orcid.org/0000-0002-1138-5634"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hsin-Tzu Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040774251","display_name":"Yu-Jung Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu-Jung Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5046617410","display_name":"Po-Chyi Su","orcid":"https://orcid.org/0000-0002-7457-8409"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Po-Chyi Su","raw_affiliation_strings":["Department of Computer Science and Information Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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/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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9991999864578247,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7706718444824219},{"id":"https://openalex.org/keywords/background-subtraction","display_name":"Background subtraction","score":0.756083607673645},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.752975583076477},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.6806485652923584},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5264445543289185},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4675556421279907},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4529378414154053},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.441660076379776},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.43082213401794434},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3878692090511322},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.17007854580879211},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1243191659450531},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.08795434236526489}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7706718444824219},{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.756083607673645},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.752975583076477},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.6806485652923584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5264445543289185},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4675556421279907},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4529378414154053},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.441660076379776},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.43082213401794434},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3878692090511322},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.17007854580879211},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1243191659450531},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.08795434236526489},{"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/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce-tw46550.2019.8991708","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-tw46550.2019.8991708","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2130293653","https://openalex.org/W2155893237","https://openalex.org/W2194775991","https://openalex.org/W4248936881"],"related_works":["https://openalex.org/W2188430267","https://openalex.org/W2610698896","https://openalex.org/W2369265144","https://openalex.org/W28439884","https://openalex.org/W2753866198","https://openalex.org/W2798919479","https://openalex.org/W1983333094","https://openalex.org/W2135883525","https://openalex.org/W2002381192","https://openalex.org/W2047028599"],"abstract_inverted_index":{"A":[0,100],"large":[1],"number":[2,102],"of":[3,22,33,68,103,134],"digital":[4],"cameras":[5,29],"have":[6],"been":[7],"installed":[8],"at":[9,42],"intersections":[10],"in":[11,126],"urban":[12,52],"areas":[13],"to":[14,76,96,130,143],"help":[15],"monitor":[16],"traffic":[17,27,53,79],"conditions.":[18],"Making":[19],"better":[20],"use":[21],"scenes":[23],"captured":[24],"by":[25,107,114],"these":[26],"surveillance":[28,58,80],"facilitates":[30],"the":[31,72,111,115,123,154],"construction":[32],"advanced":[34],"Intelligent":[35],"Transportation":[36],"Systems":[37],"(ITS).":[38],"This":[39],"research":[40],"aims":[41],"developing":[43],"an":[44,127],"adaptive":[45,147],"vehicle":[46,98,162],"detection":[47,112],"and":[48,159],"classification":[49],"scheme":[50,66,156],"for":[51,82],"scenes,":[54],"which":[55],"collects":[56],"roadside":[57],"videos":[59],"from":[60],"publicly":[61],"available":[62],"sources.":[63],"The":[64,90,138,149],"proposed":[65,155],"consists":[67],"two":[69],"main":[70],"phases;":[71],"first":[73],"phase":[74,92],"is":[75],"collect":[77],"some":[78],"images":[81],"training":[83,136,139],"a":[84,145],"general":[85,116],"model":[86],"using":[87],"Faster":[88],"R-CNN.":[89],"second":[91],"utilizes":[93],"background":[94,125],"subtraction":[95],"extract":[97],"proposals.":[99],"sufficient":[101],"vehicles":[104,119],"are":[105,120,141],"collected":[106],"comparing":[108],"proposals":[109],"with":[110],"results":[113,151],"model.":[117,148],"Collected":[118],"superimposed":[121],"on":[122],"constructed":[124],"appropriate":[128],"order":[129],"achieve":[131],"semiautomatic":[132],"generation":[133],"annotated":[135],"data.":[137],"data":[140],"used":[142],"acquire":[144],"second-phase":[146],"experimental":[150],"show":[152],"that":[153],"performs":[157],"well":[158],"can":[160],"handle":[161],"occlusion":[163],"problems.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
