{"id":"https://openalex.org/W2991461081","doi":"https://doi.org/10.1109/smc.2019.8914169","title":"Improving Deep Learning by Incorporating Semi-automatic Moving Object Annotation and Filtering for Vision-based Vehicle Detection","display_name":"Improving Deep Learning by Incorporating Semi-automatic Moving Object Annotation and Filtering for Vision-based Vehicle Detection","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2991461081","doi":"https://doi.org/10.1109/smc.2019.8914169","mag":"2991461081"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2019.8914169","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2019.8914169","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 Systems, Man and Cybernetics (SMC)","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/A5067139599","display_name":"Ri-Chen Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I99613584","display_name":"National Taipei University","ror":"https://ror.org/03e29r284","country_code":"TW","type":"education","lineage":["https://openalex.org/I99613584"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ri-Chen Feng","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan","institution_ids":["https://openalex.org/I99613584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085148868","display_name":"Daw-Tung Lin","orcid":"https://orcid.org/0000-0003-3261-806X"},"institutions":[{"id":"https://openalex.org/I99613584","display_name":"National Taipei University","ror":"https://ror.org/03e29r284","country_code":"TW","type":"education","lineage":["https://openalex.org/I99613584"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Daw-Tung Lin","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan","institution_ids":["https://openalex.org/I99613584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061319655","display_name":"Ken-Min Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I99613584","display_name":"National Taipei University","ror":"https://ror.org/03e29r284","country_code":"TW","type":"education","lineage":["https://openalex.org/I99613584"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ken-Min Chen","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan","institution_ids":["https://openalex.org/I99613584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039440131","display_name":"Yi-Yao Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I92172085","display_name":"Chunghwa Telecom (Taiwan)","ror":"https://ror.org/04f786589","country_code":"TW","type":"company","lineage":["https://openalex.org/I92172085"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yi-Yao Lin","raw_affiliation_strings":["Business Solutions Laboratory, Telecommunication Laboratories, Taoyuan, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Business Solutions Laboratory, Telecommunication Laboratories, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I92172085"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054602299","display_name":"Chin-De Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I92172085","display_name":"Chunghwa Telecom (Taiwan)","ror":"https://ror.org/04f786589","country_code":"TW","type":"company","lineage":["https://openalex.org/I92172085"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chin-De Liu","raw_affiliation_strings":["Business Solutions Laboratory, Telecommunication Laboratories, Taoyuan, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Business Solutions Laboratory, Telecommunication Laboratories, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I92172085"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1017,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.4661061,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"10","issue":null,"first_page":"2484","last_page":"2489"},"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":0.9998999834060669,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9986000061035156,"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/computer-science","display_name":"Computer science","score":0.8422905206680298},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7753493785858154},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6710606217384338},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6524152755737305},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6483885049819946},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6109615564346313},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5347098112106323},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.49100592732429504},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45438823103904724},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4335968494415283},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.361783504486084}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8422905206680298},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7753493785858154},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6710606217384338},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6524152755737305},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6483885049819946},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6109615564346313},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5347098112106323},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49100592732429504},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45438823103904724},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4335968494415283},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.361783504486084},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc.2019.8914169","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2019.8914169","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 Systems, Man and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W967544008","https://openalex.org/W1861492603","https://openalex.org/W2031489346","https://openalex.org/W2108598243","https://openalex.org/W2118246710","https://openalex.org/W2127070222","https://openalex.org/W2134670479","https://openalex.org/W2152411181","https://openalex.org/W2163605009","https://openalex.org/W2570343428","https://openalex.org/W2576289912","https://openalex.org/W2613718673","https://openalex.org/W2795376175","https://openalex.org/W2806178426","https://openalex.org/W2949933669","https://openalex.org/W2962920946","https://openalex.org/W2963150697","https://openalex.org/W2963901018","https://openalex.org/W3106250896","https://openalex.org/W4293388793","https://openalex.org/W4302061398","https://openalex.org/W6620707391","https://openalex.org/W6625168331","https://openalex.org/W6639102338","https://openalex.org/W6679792166","https://openalex.org/W6684191040","https://openalex.org/W6694605516","https://openalex.org/W6695463398","https://openalex.org/W6750066941","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2263699433","https://openalex.org/W2377979023","https://openalex.org/W2218034408","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W4320729701","https://openalex.org/W3210378990","https://openalex.org/W3034745255","https://openalex.org/W4254103348"],"abstract_inverted_index":{"Deep":[0],"learning":[1,14,82,162],"has":[2],"undergone":[3],"tremendous":[4],"advancements":[5],"in":[6,62,97],"computer":[7],"vision":[8],"studies.":[9],"The":[10,84],"training":[11,142],"of":[12,22,136,191],"deep":[13,81,161],"neural":[15],"networks":[16],"depends":[17],"on":[18,170],"a":[19,32,54,73,133,160,186],"considerable":[20],"amount":[21,135],"ground":[23,28,116],"truth":[24,29,117],"datasets.":[25],"However,":[26],"labeling":[27],"data":[30,65,123,126],"is":[31],"labor-intensive":[33],"task,":[34],"particularly":[35],"for":[36,48,59,79],"large-volume":[37],"video":[38,43,99],"analytics":[39],"applications":[40],"such":[41],"as":[42],"surveillance":[44,173],"and":[45,56,101,115,125,144],"vehicles":[46],"detection":[47,169,181],"autonomous":[49],"driving.":[50],"This":[51],"paper":[52],"presents":[53],"rapid":[55],"accurate":[57,180],"method":[58,78,86,111,131],"associative":[60],"searching":[61],"big":[63],"image":[64],"obtained":[66],"from":[67,185],"security":[68],"monitoring":[69],"systems.":[70],"We":[71,152],"developed":[72],"semi-automatic":[74],"moving":[75],"object":[76,93,95],"annotation":[77,96,118,156],"improving":[80],"models.":[83],"proposed":[85,110,130],"comprises":[87],"three":[88],"stages,":[89],"namely":[90],"automatic":[91],"foreground":[92],"extraction,":[94],"subsequent":[98],"frames,":[100],"dataset":[102,113,157],"construction":[103],"using":[104],"human-in-the-loop":[105],"quick":[106],"selection.":[107],"Furthermore,":[108],"the":[109,129,154,179],"expedites":[112],"collection":[114],"processes.":[119],"In":[120],"contrast":[121],"to":[122,158,166,193],"augmentation":[124],"generative":[127],"models,":[128],"produces":[132],"large":[134],"real":[137],"data,":[138],"which":[139],"may":[140],"facilitate":[141],"results":[143,176],"avoid":[145],"adverse":[146],"effects":[147],"engendered":[148],"by":[149],"artifactual":[150],"data.":[151],"applied":[153],"constructed":[155],"train":[159],"you-only-look-once":[163],"(YOLO)":[164],"model":[165],"perform":[167],"vehicle":[168],"street":[171],"intersection":[172],"videos.":[174],"Experimental":[175],"demonstrated":[177],"that":[178],"performance":[182],"was":[183],"improved":[184],"mean":[187],"average":[188],"precision":[189],"(mAP)":[190],"83.99":[192],"88.03.":[194]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
