{"id":"https://openalex.org/W3000999545","doi":"https://doi.org/10.1145/3373419.3373458","title":"Generating Pedestrian Training Dataset using DCGAN","display_name":"Generating Pedestrian Training Dataset using DCGAN","publication_year":2019,"publication_date":"2019-11-08","ids":{"openalex":"https://openalex.org/W3000999545","doi":"https://doi.org/10.1145/3373419.3373458","mag":"3000999545"},"language":"en","primary_location":{"id":"doi:10.1145/3373419.3373458","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3373419.3373458","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Advances in Image Processing","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/A5015224625","display_name":"Daeun Dana Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Daeun Dana Kim","raw_affiliation_strings":["Dept. of computer science and engineering, Chung-Ang University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of computer science and engineering, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009064123","display_name":"Muhammad Tanseef Shahid","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Muhammad Tanseef Shahid","raw_affiliation_strings":["Dept. of computer science and engineering, Chung-Ang University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of computer science and engineering, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057737838","display_name":"Yunseong Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yunseong Kim","raw_affiliation_strings":["Dept. of computer science and engineering, Chung-Ang University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of computer science and engineering, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100602701","display_name":"Won Jun Lee","orcid":"https://orcid.org/0000-0002-7121-057X"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Won Jun Lee","raw_affiliation_strings":["Dept. of computer science and engineering, Chung-Ang University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of computer science and engineering, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020338294","display_name":"Hyun Chul Song","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyun Chul Song","raw_affiliation_strings":["Dept. of computer science and engineering, Chung-Ang University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of computer science and engineering, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016445191","display_name":"Francesco Piccialli","orcid":"https://orcid.org/0000-0002-5179-2496"},"institutions":[{"id":"https://openalex.org/I71267560","display_name":"University of Naples Federico II","ror":"https://ror.org/05290cv24","country_code":"IT","type":"education","lineage":["https://openalex.org/I71267560"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Francesco Piccialli","raw_affiliation_strings":["Dept. of Electrical Engineering and Information Technology, University of Naples Federico II, Italy"],"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering and Information Technology, University of Naples Federico II, Italy","institution_ids":["https://openalex.org/I71267560"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038058440","display_name":"Kwang Nam Choi","orcid":"https://orcid.org/0000-0002-7420-9216"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kwang Nam Choi","raw_affiliation_strings":["Dept. of computer science and engineering, Chung-Ang University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of computer science and engineering, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5015224625"],"corresponding_institution_ids":["https://openalex.org/I67900169"],"apc_list":null,"apc_paid":null,"fwci":0.5061,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.70408114,"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":"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9997000098228455,"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":0.9976999759674072,"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/pedestrian","display_name":"Pedestrian","score":0.7748561501502991},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7578822374343872},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.694477915763855},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6793116331100464},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6583050489425659},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6475766897201538},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6449117660522461},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6405109167098999},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6140739917755127},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46854910254478455},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44772109389305115},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4216478765010834},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3620631694793701},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09292376041412354},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.08761486411094666},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.054438620805740356}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7748561501502991},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7578822374343872},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.694477915763855},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6793116331100464},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6583050489425659},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6475766897201538},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6449117660522461},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6405109167098999},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6140739917755127},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46854910254478455},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44772109389305115},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4216478765010834},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3620631694793701},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09292376041412354},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.08761486411094666},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.054438620805740356},{"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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3373419.3373458","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3373419.3373458","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Advances in Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"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/W1483870316","https://openalex.org/W2102605133","https://openalex.org/W2145339207","https://openalex.org/W2173520492","https://openalex.org/W2407521645","https://openalex.org/W2613718673","https://openalex.org/W2782143633","https://openalex.org/W2963037989","https://openalex.org/W3106250896","https://openalex.org/W4301076899","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4321369474","https://openalex.org/W2731899572","https://openalex.org/W3133861977","https://openalex.org/W3111016073","https://openalex.org/W4200173597","https://openalex.org/W3116150086","https://openalex.org/W2558093053","https://openalex.org/W2999805992","https://openalex.org/W4312417841","https://openalex.org/W2911294201"],"abstract_inverted_index":{"Recently,":[0],"as":[1],"autonomous":[2,18],"cars":[3],"are":[4,67],"developing":[5],"very":[6,68],"fast,":[7],"it":[8],"is":[9,73,93],"the":[10,61,113,128],"most":[11],"crucial":[12],"task":[13],"to":[14,83,95,111],"detect":[15],"pedestrians":[16],"for":[17,47,127],"driving.":[19],"Convolution":[20],"neural":[21],"network":[22],"based":[23,74],"on":[24,75],"pedestrian":[25,98,116],"detection":[26],"models":[27,37],"has":[28],"gained":[29],"enormous":[30],"success":[31],"in":[32,108],"many":[33],"applications.":[34],"However,":[35],"these":[36],"need":[38],"a":[39],"large":[40],"amount":[41],"of":[42,53,65,115],"annotated":[43],"and":[44,55,63],"labeled":[45],"datasets":[46,66],"training":[48,59,109,129],"process":[49],"which":[50],"requires":[51],"lots":[52],"time":[54],"human":[56],"effort.":[57],"For":[58],"samples,":[60],"diversity":[62],"quantity":[64],"important.":[69],"The":[70,102],"proposed":[71],"framework":[72,92],"Deep":[76],"Convolutional":[77],"Generative":[78],"Adversarial":[79],"Networks":[80],"(DCGAN),":[81],"able":[82,94],"generate":[84],"realistic":[85],"pedestrians.":[86],"Experimental":[87],"results":[88],"show":[89],"that":[90],"DCGAN":[91],"synthesize":[96],"real":[97],"images":[99,119],"with":[100],"diversity.":[101],"synthesized":[103],"samples":[104],"can":[105],"be":[106],"included":[107],"data":[110],"improve":[112],"performance":[114],"detectors.":[117],"24,770":[118],"including":[120],"PETA":[121],"dataset,":[122],"Inria":[123],"dataset":[124],"were":[125],"used":[126],"process.":[130]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}