{"id":"https://openalex.org/W2037547340","doi":"https://doi.org/10.1145/2808492.2808524","title":"A pedestrian and vehicle rapid identification model based on convolutional neural network","display_name":"A pedestrian and vehicle rapid identification model based on convolutional neural network","publication_year":2015,"publication_date":"2015-08-19","ids":{"openalex":"https://openalex.org/W2037547340","doi":"https://doi.org/10.1145/2808492.2808524","mag":"2037547340"},"language":"en","primary_location":{"id":"doi:10.1145/2808492.2808524","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2808492.2808524","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Internet Multimedia Computing and Service","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/A5080683301","display_name":"Ruochen Wang","orcid":"https://orcid.org/0000-0003-2014-4282"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruochen Wang","raw_affiliation_strings":["Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101723035","display_name":"Zhe Xu","orcid":"https://orcid.org/0000-0002-1647-8100"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Xu","raw_affiliation_strings":["Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5080683301"],"corresponding_institution_ids":["https://openalex.org/I37796252"],"apc_list":null,"apc_paid":null,"fwci":0.8349465,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.7969896,"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":"4"},"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":0.9987999796867371,"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":0.9959999918937683,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8478320837020874},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8107131719589233},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6909064054489136},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6758072376251221},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.6396986842155457},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5744569897651672},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5156527757644653},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.493550181388855},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4705774188041687},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4676992893218994},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4552518129348755},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4537719190120697},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4333621859550476},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.4329533576965332},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39600521326065063},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.3577110469341278},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13258790969848633}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8478320837020874},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8107131719589233},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6909064054489136},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6758072376251221},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.6396986842155457},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5744569897651672},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5156527757644653},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.493550181388855},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4705774188041687},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4676992893218994},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4552518129348755},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4537719190120697},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4333621859550476},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.4329533576965332},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39600521326065063},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.3577110469341278},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13258790969848633},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","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/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2808492.2808524","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2808492.2808524","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Internet Multimedia Computing and Service","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W1487583988","https://openalex.org/W2092985495","https://openalex.org/W2097117768","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2144513243","https://openalex.org/W2150066425","https://openalex.org/W2151103935","https://openalex.org/W2161969291"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2101960027","https://openalex.org/W2197846993","https://openalex.org/W49697837","https://openalex.org/W2586575957","https://openalex.org/W3122828758","https://openalex.org/W2170799233","https://openalex.org/W2768112316","https://openalex.org/W3014558862"],"abstract_inverted_index":{"Image":[0],"recognition":[1,25,64,131,154],"technology":[2],"based":[3],"on":[4,136],"convolutional":[5],"neural":[6],"network":[7],"(CNN)":[8],"has":[9],"been":[10],"widely":[11],"used":[12],"in":[13,19,26,50,150],"the":[14,23,27,39,51,70,81,84,98,105,109,124,130,137,142,147],"field":[15,28],"of":[16,29,41,83,91,100,112,132,146,152],"intelligent":[17,30],"transportation":[18,31],"recent":[20],"years.":[21],"Since":[22],"image":[24,101],"needs":[32],"high":[33],"real-time":[34,110],"performance,":[35],"this":[36,115],"requires":[37],"improving":[38],"speed":[40],"CNN.":[42,113],"We":[43,66],"refer":[44],"to":[45,58],"Overfeat,":[46],"which":[47],"was":[48],"proposed":[49],"ImageNet":[52],"Large":[53],"Scale":[54],"Visual":[55],"Recognition":[56],"Challenge,":[57],"build":[59],"a":[60,89,120],"vehicle":[61],"and":[62,108,123,134,144,156],"pedestrian":[63],"model.":[65],"do":[67],"not":[68],"use":[69,118],"traditional":[71,125],"sliding":[72,126],"window":[73,127],"method.":[74],"Instead,":[75],"we":[76,117,140],"apply":[77],"each":[78],"convolution":[79],"over":[80],"extent":[82],"full":[85],"image,":[86],"eventually":[87],"producing":[88],"map":[90],"output":[92],"class":[93],"predictions.":[94],"This":[95],"method":[96,122,128],"ensures":[97],"accuracy":[99],"recognition,":[102],"while":[103],"enhancing":[104],"operational":[106],"efficiency":[107],"performance":[111],"In":[114],"paper,":[116],"both":[119],"new":[121],"for":[129],"pedestrians":[133],"cars":[135],"road.":[138],"Then,":[139],"compare":[141],"advantages":[143],"disadvantages":[145],"two":[148],"methods":[149],"terms":[151],"their":[153],"effect":[155],"speed.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
