{"id":"https://openalex.org/W2970685153","doi":"https://doi.org/10.3390/sym11091081","title":"Visual Meterstick: Preceding Vehicle Ranging Using Monocular Vision Based on the Fitting Method","display_name":"Visual Meterstick: Preceding Vehicle Ranging Using Monocular Vision Based on the Fitting Method","publication_year":2019,"publication_date":"2019-08-28","ids":{"openalex":"https://openalex.org/W2970685153","doi":"https://doi.org/10.3390/sym11091081","mag":"2970685153"},"language":"en","primary_location":{"id":"doi:10.3390/sym11091081","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym11091081","pdf_url":"https://www.mdpi.com/2073-8994/11/9/1081/pdf?version=1567080988","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/11/9/1081/pdf?version=1567080988","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056076734","display_name":"Chaochao Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I114234892","display_name":"Beijing Union University","ror":"https://ror.org/01hg31662","country_code":"CN","type":"education","lineage":["https://openalex.org/I114234892"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaochao Meng","raw_affiliation_strings":["Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China","institution_ids":["https://openalex.org/I114234892"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057248354","display_name":"Hong Bao","orcid":"https://orcid.org/0000-0002-4760-1570"},"institutions":[{"id":"https://openalex.org/I114234892","display_name":"Beijing Union University","ror":"https://ror.org/01hg31662","country_code":"CN","type":"education","lineage":["https://openalex.org/I114234892"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hong Bao","raw_affiliation_strings":["Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China","institution_ids":["https://openalex.org/I114234892"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100694241","display_name":"Yan Ma","orcid":"https://orcid.org/0000-0001-9243-8322"},"institutions":[{"id":"https://openalex.org/I114234892","display_name":"Beijing Union University","ror":"https://ror.org/01hg31662","country_code":"CN","type":"education","lineage":["https://openalex.org/I114234892"]},{"id":"https://openalex.org/I25757504","display_name":"China University of Mining and Technology","ror":"https://ror.org/01xt2dr21","country_code":"CN","type":"education","lineage":["https://openalex.org/I25757504"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Ma","raw_affiliation_strings":["Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China","School of Mechanical Electronic &amp; Information Engineering, China University of Mining &amp; Technology, Beijing 100083, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China","institution_ids":["https://openalex.org/I114234892"]},{"raw_affiliation_string":"School of Mechanical Electronic &amp; Information Engineering, China University of Mining &amp; Technology, Beijing 100083, China","institution_ids":["https://openalex.org/I25757504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005344603","display_name":"Xinkai Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I114234892","display_name":"Beijing Union University","ror":"https://ror.org/01hg31662","country_code":"CN","type":"education","lineage":["https://openalex.org/I114234892"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinkai Xu","raw_affiliation_strings":["Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China","institution_ids":["https://openalex.org/I114234892"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100379931","display_name":"Yuqing Li","orcid":"https://orcid.org/0000-0002-0275-4004"},"institutions":[{"id":"https://openalex.org/I114234892","display_name":"Beijing Union University","ror":"https://ror.org/01hg31662","country_code":"CN","type":"education","lineage":["https://openalex.org/I114234892"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqing Li","raw_affiliation_strings":["Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China","institution_ids":["https://openalex.org/I114234892"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5057248354"],"corresponding_institution_ids":["https://openalex.org/I114234892"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.8913,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.77231845,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"11","issue":"9","first_page":"1081","last_page":"1081"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998000264167786,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.885461688041687},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8053526282310486},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7731823921203613},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7178689241409302},{"id":"https://openalex.org/keywords/monocular-vision","display_name":"Monocular vision","score":0.6298428773880005},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.5525253415107727},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.5115688443183899},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5027592182159424},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4800221025943756},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46387019753456116},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.460137277841568},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.42886143922805786},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.42378565669059753},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.42351412773132324},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3295941948890686}],"concepts":[{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.885461688041687},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8053526282310486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7731823921203613},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7178689241409302},{"id":"https://openalex.org/C158829959","wikidata":"https://www.wikidata.org/wiki/Q1640606","display_name":"Monocular vision","level":2,"score":0.6298428773880005},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.5525253415107727},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.5115688443183899},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5027592182159424},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4800221025943756},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46387019753456116},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.460137277841568},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.42886143922805786},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.42378565669059753},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.42351412773132324},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3295941948890686},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/sym11091081","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym11091081","pdf_url":"https://www.mdpi.com/2073-8994/11/9/1081/pdf?version=1567080988","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6fb18991d241442ab3b7443ba85e6269","is_oa":true,"landing_page_url":"https://doaj.org/article/6fb18991d241442ab3b7443ba85e6269","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 11, Iss 9, p 1081 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2073-8994/11/9/1081/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/sym11091081","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/sym11091081","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym11091081","pdf_url":"https://www.mdpi.com/2073-8994/11/9/1081/pdf?version=1567080988","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2970685153.pdf","grobid_xml":"https://content.openalex.org/works/W2970685153.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W49517504","https://openalex.org/W1549739843","https://openalex.org/W1979387426","https://openalex.org/W2014736901","https://openalex.org/W2070403512","https://openalex.org/W2080873731","https://openalex.org/W2130799764","https://openalex.org/W2146173477","https://openalex.org/W2168215035","https://openalex.org/W2613718673","https://openalex.org/W2799352588","https://openalex.org/W2806070179","https://openalex.org/W2905313739","https://openalex.org/W2963150697","https://openalex.org/W6668144610"],"related_works":["https://openalex.org/W4237171675","https://openalex.org/W3036286480","https://openalex.org/W4287027631","https://openalex.org/W3192357901","https://openalex.org/W2387360586","https://openalex.org/W4386996071","https://openalex.org/W2952736415","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W2883297582"],"abstract_inverted_index":{"The":[0,145],"gradual":[1],"application":[2],"of":[3,9,39,103,132,141,147,154],"deep":[4,80],"learning":[5],"in":[6,61,76,107],"the":[7,37,40,62,65,77,95,100,104,108,110,116,126,130,133,148,152],"field":[8],"computer":[10],"vision":[11],"and":[12,25,74],"image":[13,26],"processing":[14],"has":[15],"made":[16],"great":[17],"breakthroughs.":[18],"Applications":[19],"such":[20],"as":[21],"object":[22],"detection,":[23,64],"recognition":[24],"semantic":[27],"segmentation":[28],"have":[29],"been":[30],"improved.":[31],"In":[32],"this":[33],"study,":[34],"to":[35,99,115],"measure":[36,129],"distance":[38,131],"vehicle":[41,45,63,88,117,135],"ahead,":[42],"a":[43,138],"preceding":[44,134],"ranging":[46,139],"system":[47],"based":[48],"on":[49],"fitting":[50,111],"method":[51,85,112,127],"was":[52,72,113],"designed.":[53],"First":[54],"obtaining":[55],"an":[56],"accurate":[57],"bounding":[58,105],"box":[59,106],"frame":[60],"Mask":[66],"R-CNN":[67],"(region-convolutional":[68],"neural":[69],"networks)":[70],"algorithm":[71],"improved":[73],"tested":[75],"BDD100K":[78],"(Berkeley":[79],"derive)":[81],"asymmetry":[82],"dataset.":[83],"This":[84],"can":[86,128],"shorten":[87],"detection":[89],"time":[90],"by":[91],"33%":[92],"without":[93],"reducing":[94],"accuracy.":[96],"Then,":[97],"according":[98],"pixel":[101],"value":[102],"image,":[109],"applied":[114],"monocular":[118],"camera":[119],"for":[120,157],"ranging.":[121],"Experimental":[122],"results":[123,150],"demonstrate":[124],"that":[125],"effectively,":[136],"with":[137],"error":[140],"less":[142],"than":[143],"10%.":[144],"accuracy":[146],"measurement":[149],"meets":[151],"requirements":[153],"collision":[155],"warning":[156],"safe":[158],"driving.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
