{"id":"https://openalex.org/W3126497165","doi":"https://doi.org/10.1109/icpr48806.2021.9412764","title":"Robust pedestrian detection in thermal imagery using synthesized images","display_name":"Robust pedestrian detection in thermal imagery using synthesized images","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3126497165","doi":"https://doi.org/10.1109/icpr48806.2021.9412764","mag":"3126497165"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412764","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412764","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2102.02005","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090524265","display_name":"My Kieu","orcid":"https://orcid.org/0000-0002-7813-5744"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"My Kieu","raw_affiliation_strings":["MICC - Universit\u00e0 degli Studi di Firenze"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MICC - Universit\u00e0 degli Studi di Firenze","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052591162","display_name":"Lorenzo Berlincioni","orcid":"https://orcid.org/0000-0001-6131-1505"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Lorenzo Berlincioni","raw_affiliation_strings":["MICC - Universit\u00e0 degli Studi di Firenze"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MICC - Universit\u00e0 degli Studi di Firenze","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038181828","display_name":"Leonardo Galteri","orcid":"https://orcid.org/0000-0002-7247-9407"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Leonardo Galteri","raw_affiliation_strings":["MICC - Universit\u00e0 degli Studi di Firenze"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MICC - Universit\u00e0 degli Studi di Firenze","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053986996","display_name":"Marco Bertini","orcid":"https://orcid.org/0000-0002-1364-218X"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Marco Bertini","raw_affiliation_strings":["MICC - Universit\u00e0 degli Studi di Firenze"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MICC - Universit\u00e0 degli Studi di Firenze","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064029620","display_name":"Andrew D. Bagdanov","orcid":"https://orcid.org/0000-0001-6408-7043"},"institutions":[{"id":"https://openalex.org/I4210112122","display_name":"Florence (Netherlands)","ror":"https://ror.org/021qg3d64","country_code":"NL","type":"company","lineage":["https://openalex.org/I4210112122"]},{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT","NL"],"is_corresponding":false,"raw_author_name":"Andrew D. Bagdanov","raw_affiliation_strings":["MICC - Universit\u00e0 degli Studi di Firenze","Univ of Florence"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MICC - Universit\u00e0 degli Studi di Firenze","institution_ids":["https://openalex.org/I45084792"]},{"raw_affiliation_string":"Univ of Florence","institution_ids":["https://openalex.org/I4210112122"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081506611","display_name":"Alberto Del Bimbo","orcid":"https://orcid.org/0000-0002-1052-8322"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Alberto del Bimbo","raw_affiliation_strings":["MICC - Universit\u00e0 degli Studi di Firenze"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MICC - Universit\u00e0 degli Studi di Firenze","institution_ids":["https://openalex.org/I45084792"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8804","last_page":"8811"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9991999864578247,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9991999864578247,"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-detection","display_name":"Pedestrian detection","score":0.8596739768981934},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.77784264087677},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.7094327211380005},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7032372951507568},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6553288698196411},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5864250063896179},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.544353187084198},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5123249292373657},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4400685727596283},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39612361788749695},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.335116446018219},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3269606828689575},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.13389179110527039},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10682615637779236}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8596739768981934},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.77784264087677},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.7094327211380005},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7032372951507568},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6553288698196411},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5864250063896179},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.544353187084198},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5123249292373657},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4400685727596283},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39612361788749695},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.335116446018219},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3269606828689575},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.13389179110527039},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10682615637779236},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412764","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412764","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2102.02005","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2102.02005","pdf_url":"https://arxiv.org/pdf/2102.02005","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:3126497165","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2102.02005","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2102.02005","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2102.02005","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2102.02005","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2102.02005","pdf_url":"https://arxiv.org/pdf/2102.02005","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5099999904632568,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":82,"referenced_works":["https://openalex.org/W1598330724","https://openalex.org/W1903127635","https://openalex.org/W1910108985","https://openalex.org/W1973788353","https://openalex.org/W2031454541","https://openalex.org/W2099471712","https://openalex.org/W2153663612","https://openalex.org/W2194775991","https://openalex.org/W2265127172","https://openalex.org/W2331128040","https://openalex.org/W2479644247","https://openalex.org/W2549063375","https://openalex.org/W2555437177","https://openalex.org/W2570343428","https://openalex.org/W2593414223","https://openalex.org/W2608295741","https://openalex.org/W2734706361","https://openalex.org/W2741620214","https://openalex.org/W2743197123","https://openalex.org/W2796347433","https://openalex.org/W2802386777","https://openalex.org/W2805898668","https://openalex.org/W2805902878","https://openalex.org/W2807577763","https://openalex.org/W2807758380","https://openalex.org/W2887564556","https://openalex.org/W2887656403","https://openalex.org/W2891158090","https://openalex.org/W2894737833","https://openalex.org/W2898091194","https://openalex.org/W2902314041","https://openalex.org/W2904856314","https://openalex.org/W2905406437","https://openalex.org/W2908984097","https://openalex.org/W2913527057","https://openalex.org/W2913889214","https://openalex.org/W2920991918","https://openalex.org/W2921633805","https://openalex.org/W2946489910","https://openalex.org/W2952773607","https://openalex.org/W2962687329","https://openalex.org/W2962793481","https://openalex.org/W2962808524","https://openalex.org/W2963073614","https://openalex.org/W2963174698","https://openalex.org/W2963188557","https://openalex.org/W2963214698","https://openalex.org/W2963226019","https://openalex.org/W2963330667","https://openalex.org/W2963372104","https://openalex.org/W2963446712","https://openalex.org/W2963470893","https://openalex.org/W2963579094","https://openalex.org/W2963767194","https://openalex.org/W2963800363","https://openalex.org/W2963805028","https://openalex.org/W2964027659","https://openalex.org/W2964167449","https://openalex.org/W2965912241","https://openalex.org/W2966197049","https://openalex.org/W2966379441","https://openalex.org/W2970929725","https://openalex.org/W2971938076","https://openalex.org/W2981132188","https://openalex.org/W3016126079","https://openalex.org/W3035231706","https://openalex.org/W3102472504","https://openalex.org/W3102667960","https://openalex.org/W6687506355","https://openalex.org/W6692550842","https://openalex.org/W6702130928","https://openalex.org/W6718140377","https://openalex.org/W6720963587","https://openalex.org/W6729881831","https://openalex.org/W6730523353","https://openalex.org/W6745992979","https://openalex.org/W6746282794","https://openalex.org/W6753836424","https://openalex.org/W6754405603","https://openalex.org/W6755837410","https://openalex.org/W6756834165","https://openalex.org/W6963841193"],"related_works":["https://openalex.org/W1167932326","https://openalex.org/W2800406466","https://openalex.org/W2791482973","https://openalex.org/W2772004441","https://openalex.org/W3004460637","https://openalex.org/W2924137256","https://openalex.org/W3049650526","https://openalex.org/W3010943027","https://openalex.org/W2802652728","https://openalex.org/W2737490232","https://openalex.org/W2798965597","https://openalex.org/W2970829534","https://openalex.org/W3046215620","https://openalex.org/W3119087484","https://openalex.org/W2790549725","https://openalex.org/W2790561122","https://openalex.org/W2883955876","https://openalex.org/W3186188398","https://openalex.org/W3029267566","https://openalex.org/W2600222597"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3],"propose":[4],"a":[5,19,27,87],"method":[6,30],"for":[7,74],"improving":[8],"pedestrian":[9,37,71,90],"detection":[10,93,187],"in":[11,83,94,125,159],"the":[12,43,65,95,101,126,136,155,176,184,194],"thermal":[13,53,70,113,146],"domain":[14,28,127],"using":[15,31,106],"two":[16],"stages:":[17],"first,":[18],"generative":[20,79],"data":[21,33,80,120,147,157,173],"augmentation":[22,81],"approach":[23],"is":[24,48,148],"used,":[25],"then":[26,61],"adaptation":[29,128],"generated":[32,121,152],"adapts":[34],"an":[35,169],"RGB":[36,57],"detector.":[38],"Our":[39],"model,":[40],"based":[41],"on":[42,118,135,189],"Least-Squares":[44],"Generative":[45],"Adversarial":[46],"Network,":[47],"trained":[49],"to":[50,63,85,92,154,193],"synthesize":[51],"realistic":[52],"versions":[54],"of":[55,68,103,110,172,178],"input":[56],"images":[58,72,153,166],"which":[59],"are":[60],"used":[62],"augment":[64],"limited":[66],"amount":[67],"labeled":[69],"available":[73,111,149],"training.":[75],"We":[76],"apply":[77],"our":[78,104,123,130,179,181],"strategy":[82],"order":[84],"adapt":[86],"pretrained":[88],"YOLOv3":[89],"detector":[91,131,182],"thermal-only":[96],"domain.":[97],"Experimental":[98],"results":[99,134,158,188],"demonstrate":[100],"effectiveness":[102],"approach:":[105],"less":[107],"than":[108],"50%":[109],"real":[112,145],"training":[114,156],"data,":[115],"and":[116],"relying":[117],"synthesized":[119],"by":[122],"model":[124],"phase,":[129],"achieves":[132,183],"state-of-the-art":[133],"KAIST":[137,190],"Multispectral":[138],"Pedestrian":[139],"Detection":[140],"Benchmark;":[141],"even":[142],"if":[143],"more":[144],"adding":[150],"GAN":[151],"improved":[160],"performance,":[161],"thus":[162],"showing":[163],"that":[164],"these":[165],"act":[167],"as":[168],"effective":[170],"form":[171],"augmentation.":[174],"To":[175],"best":[177,185],"knowledge,":[180],"single-modality":[186],"with":[191],"respect":[192],"state-of-the-art.":[195]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
