{"id":"https://openalex.org/W4389104769","doi":"https://doi.org/10.1109/tim.2023.3336760","title":"Image-Level Automatic Data Augmentation for Pedestrian Detection","display_name":"Image-Level Automatic Data Augmentation for Pedestrian Detection","publication_year":2023,"publication_date":"2023-11-28","ids":{"openalex":"https://openalex.org/W4389104769","doi":"https://doi.org/10.1109/tim.2023.3336760"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2023.3336760","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3336760","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-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/A5085684328","display_name":"Yunfeng Ma","orcid":"https://orcid.org/0009-0007-4539-2043"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yunfeng Ma","raw_affiliation_strings":["College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343854","display_name":"Min Liu","orcid":"https://orcid.org/0000-0001-6406-4896"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Liu","raw_affiliation_strings":["College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028769645","display_name":"Yi Tang","orcid":"https://orcid.org/0000-0003-4006-428X"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Tang","raw_affiliation_strings":["College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100683380","display_name":"Xueping Wang","orcid":"https://orcid.org/0000-0003-4862-8975"},"institutions":[{"id":"https://openalex.org/I173759888","display_name":"Hunan Normal University","ror":"https://ror.org/053w1zy07","country_code":"CN","type":"education","lineage":["https://openalex.org/I173759888"]},{"id":"https://openalex.org/I3130607311","display_name":"Changsha Normal University","ror":"https://ror.org/02sqk3z62","country_code":"CN","type":"education","lineage":["https://openalex.org/I3130607311"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueping Wang","raw_affiliation_strings":["College of Information Science and Engineering, Hunan Normal University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Hunan Normal University, Changsha, China","institution_ids":["https://openalex.org/I3130607311","https://openalex.org/I173759888"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025640070","display_name":"Yaonan Wang","orcid":"https://orcid.org/0000-0002-0519-6458"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaonan Wang","raw_affiliation_strings":["College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5085684328"],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":0.4779,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.66130716,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"73","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9988999962806702,"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":0.9988999962806702,"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":0.9976999759674072,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9968000054359436,"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":0.7589894533157349},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.6690779328346252},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.644879937171936},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5955283641815186},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5401408076286316},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47977858781814575},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.477977454662323},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4333956837654114},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.42896467447280884},{"id":"https://openalex.org/keywords/data-cleansing","display_name":"Data cleansing","score":0.4244350790977478},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.37167680263519287},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33991920948028564},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.331440269947052},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.13689029216766357},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10260006785392761}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7589894533157349},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.6690779328346252},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.644879937171936},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5955283641815186},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5401408076286316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47977858781814575},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.477977454662323},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4333956837654114},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.42896467447280884},{"id":"https://openalex.org/C42199009","wikidata":"https://www.wikidata.org/wiki/Q1172378","display_name":"Data cleansing","level":4,"score":0.4244350790977478},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.37167680263519287},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33991920948028564},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.331440269947052},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.13689029216766357},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10260006785392761},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2023.3336760","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3336760","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G1085509035","display_name":null,"funder_award_id":"62221002","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7816055531","display_name":null,"funder_award_id":"U22B2050","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1686810756","https://openalex.org/W2090839732","https://openalex.org/W2151238122","https://openalex.org/W2177640694","https://openalex.org/W2194775991","https://openalex.org/W2565639579","https://openalex.org/W2594507094","https://openalex.org/W2612624696","https://openalex.org/W2743905801","https://openalex.org/W2775890136","https://openalex.org/W2792824754","https://openalex.org/W2798542761","https://openalex.org/W2883363148","https://openalex.org/W2895077992","https://openalex.org/W2949736877","https://openalex.org/W2963150697","https://openalex.org/W2963299996","https://openalex.org/W2963681621","https://openalex.org/W2963769056","https://openalex.org/W2964093967","https://openalex.org/W2964121718","https://openalex.org/W2995649684","https://openalex.org/W3000999545","https://openalex.org/W3007827672","https://openalex.org/W3009184501","https://openalex.org/W3034638324","https://openalex.org/W3035323039","https://openalex.org/W3040710053","https://openalex.org/W3092462694","https://openalex.org/W3096609285","https://openalex.org/W3097975205","https://openalex.org/W3106753828","https://openalex.org/W3127553939","https://openalex.org/W3135934332","https://openalex.org/W3173632533","https://openalex.org/W3202867260","https://openalex.org/W3203596689","https://openalex.org/W3204913553","https://openalex.org/W4205276794","https://openalex.org/W4206389547","https://openalex.org/W4221159372","https://openalex.org/W4285210396","https://openalex.org/W4288375226","https://openalex.org/W4296437452","https://openalex.org/W4312506424","https://openalex.org/W4366668491","https://openalex.org/W6637373629","https://openalex.org/W6676179485","https://openalex.org/W6750163814","https://openalex.org/W6750697433","https://openalex.org/W6762334975","https://openalex.org/W6762619590","https://openalex.org/W6784094891"],"related_works":["https://openalex.org/W2997590552","https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W4240309520","https://openalex.org/W2571761423","https://openalex.org/W2101960027","https://openalex.org/W4386799044","https://openalex.org/W2197846993","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"Data":[0],"augmentation":[1,68],"(DA)":[2],"is":[3,212],"a":[4,31,43,136,197,206],"commonly":[5,230],"used":[6,231],"method":[7,105,133,177,234,240],"to":[8,28,48,55,76,84,108,121,154,178,191,195],"alleviate":[9],"the":[10,38,56,77,94,118,129,147,180,188,229,245,259,267,274],"problem":[11],"of":[12,33,45,89,149,182,244],"detecting":[13],"occluded":[14],"pedestrians":[15],"in":[16,62,117],"crowded":[17],"scenes.":[18],"Recently,":[19],"several":[20],"dataset-level":[21,67],"automatic":[22,103,131,232],"DA":[23,35,51,73,104,112,132,233],"methods":[24,69,272],"have":[25],"been":[26],"proposed":[27],"search":[29,137,207,246],"for":[30,37,114,214,235],"set":[32],"general":[34],"policies":[36,74,185],"entire":[39],"dataset,":[40,269],"which":[41,81,144],"saves":[42],"lot":[44],"time":[46],"compared":[47,227],"manually":[49],"designing":[50],"policies.":[52,204],"However,":[53],"due":[54],"huge":[57],"differences":[58,78,95],"between":[59,79,96,201],"each":[60,97,115],"image":[61,116],"pedestrian":[63,236],"detection":[64,251],"datasets,":[65],"existing":[66],"cannot":[70],"automatically":[71],"adjust":[72],"according":[75,120,153],"them,":[80],"will":[82],"lead":[83],"outlier":[85],"data":[86],"and":[87,166,184,186,203,222,263],"degradation":[88],"model":[90],"performance.":[91],"Therefore,":[92],"considering":[93],"image,":[98],"we":[99,126,145,173,254],"propose":[100],"an":[101,110,175],"image-level":[102,130],"that":[106,159,226],"aims":[107],"find":[109],"optimal":[111],"policy":[113,216],"dataset":[119],"their":[122,155],"respective":[123,156],"characteristics.":[124],"Specifically,":[125,253],"first":[127],"reformulate":[128],"by":[134],"constructing":[135],"space":[138],"based":[139],"on":[140,220,258,266,273],"categorical":[141],"distribution,":[142],"within":[143],"specify":[146],"probability":[148],"operations":[150,161,168],"being":[151],"sampled":[152],"effectiveness":[157],"so":[158],"useful":[160],"can":[162,169],"be":[163,170],"effectively":[164],"preserved":[165],"useless":[167],"suppressed.":[171],"Subsequently,":[172],"design":[174],"encoding":[176],"recode":[179],"index":[181,190],"images":[183,202],"use":[187],"encoded":[189],"closely":[192],"associate":[193],"them":[194],"achieve":[196,255],"stable":[198],"matching":[199],"relationship":[200],"Finally,":[205],"framework":[208],"with":[209,228],"Bayesian":[210],"optimization":[211],"developed":[213],"efficient":[215],"mining.":[217],"Comprehensive":[218],"experiments":[219],"CrowdHuman":[221,268,275],"CityPersons":[223,260],"datasets":[224],"show":[225],"detection,":[237],"AutoPedestrian,":[238],"our":[239],"takes":[241],"only":[242],"1/14":[243],"time,":[247],"but":[248],"achieves":[249],"better":[250],"accuracy.":[252],"10.2%":[256],"MR\u22122":[257,265],"reasonable":[261],"subset":[262],"36.8%":[264],"outperforming":[270],"state-of-the-art":[271],"dataset.":[276]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}