{"id":"https://openalex.org/W3118871075","doi":"https://doi.org/10.1109/ssci47803.2020.9308531","title":"Automatic annotation of pedestrians in thermal images using background/foreground segmentation for training deep neural networks","display_name":"Automatic annotation of pedestrians in thermal images using background/foreground segmentation for training deep neural networks","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3118871075","doi":"https://doi.org/10.1109/ssci47803.2020.9308531","mag":"3118871075"},"language":"en","primary_location":{"id":"doi:10.1109/ssci47803.2020.9308531","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci47803.2020.9308531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5080421930","display_name":"Zuhaib Ahmed Shaikh","orcid":null},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Zuhaib Ahmed Shaikh","raw_affiliation_strings":["TELIN-IPI, Ghent University \u2013 imec, Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TELIN-IPI, Ghent University \u2013 imec, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074485158","display_name":"Gianni Allebosch","orcid":"https://orcid.org/0000-0003-2502-3746"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Gianni Allebosch","raw_affiliation_strings":["TELIN-IPI, Ghent University \u2013 imec, Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TELIN-IPI, Ghent University \u2013 imec, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068050252","display_name":"Peter Veelaert","orcid":"https://orcid.org/0000-0003-4746-9087"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Peter Veelaert","raw_affiliation_strings":["TELIN-IPI, Ghent University \u2013 imec, Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TELIN-IPI, Ghent University \u2013 imec, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071483672","display_name":"Wilfried Philips","orcid":"https://orcid.org/0000-0003-4456-4353"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Philips Wilfried","raw_affiliation_strings":["TELIN-IPI, Ghent University \u2013 imec, Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TELIN-IPI, Ghent University \u2013 imec, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I32597200"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987999796867371,"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":0.8598051071166992},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8386222124099731},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6913776993751526},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6176162362098694},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5712168216705322},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.562231183052063},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5454259514808655},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5207347869873047},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5127345323562622},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5006377696990967},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45230036973953247},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.443350225687027},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.42907214164733887},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42790770530700684},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4235663115978241},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.3396785259246826}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8598051071166992},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8386222124099731},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6913776993751526},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6176162362098694},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5712168216705322},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.562231183052063},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5454259514808655},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5207347869873047},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5127345323562622},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5006377696990967},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45230036973953247},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.443350225687027},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.42907214164733887},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42790770530700684},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4235663115978241},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.3396785259246826},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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":2,"locations":[{"id":"doi:10.1109/ssci47803.2020.9308531","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci47803.2020.9308531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},{"id":"pmh:oai:archive.ugent.be:8691354","is_oa":false,"landing_page_url":"https://biblio.ugent.be/publication/8691354","pdf_url":null,"source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISBN: 9781728125473","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W21006490","https://openalex.org/W54398672","https://openalex.org/W639708223","https://openalex.org/W1486239599","https://openalex.org/W1514027499","https://openalex.org/W1686810756","https://openalex.org/W1797070008","https://openalex.org/W2000789130","https://openalex.org/W2049409070","https://openalex.org/W2070789837","https://openalex.org/W2098305432","https://openalex.org/W2108598243","https://openalex.org/W2131081720","https://openalex.org/W2136132422","https://openalex.org/W2138857742","https://openalex.org/W2163605009","https://openalex.org/W2271840356","https://openalex.org/W2394684092","https://openalex.org/W2613718673","https://openalex.org/W2626445779","https://openalex.org/W2793406320","https://openalex.org/W2794219463","https://openalex.org/W2800388963","https://openalex.org/W2802368751","https://openalex.org/W2895703551","https://openalex.org/W2900482393","https://openalex.org/W2954996726","https://openalex.org/W2963346784","https://openalex.org/W2963499153","https://openalex.org/W2999952152","https://openalex.org/W3091905774","https://openalex.org/W6600827882","https://openalex.org/W6602170065","https://openalex.org/W6620707391","https://openalex.org/W6630792627","https://openalex.org/W6637373629","https://openalex.org/W6638183220","https://openalex.org/W6680300913","https://openalex.org/W6684191040","https://openalex.org/W6694517276","https://openalex.org/W6711632521","https://openalex.org/W6739369274","https://openalex.org/W6740580305","https://openalex.org/W6783596713"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2802018156","https://openalex.org/W4313315626","https://openalex.org/W2101531944","https://openalex.org/W2922437833","https://openalex.org/W2100052226","https://openalex.org/W4312696271","https://openalex.org/W4223892596","https://openalex.org/W2933098581"],"abstract_inverted_index":{"Deep":[0],"Neural":[1],"Networks":[2],"for":[3,65,84,114,175],"object":[4],"detection":[5,116],"have":[6,52,75],"become":[7],"of":[8,39,142],"significant":[9],"interest":[10],"with":[11,177],"the":[12,70,140],"substantial":[13],"improvement":[14],"in":[15,20,37,117,132],"their":[16,21],"efficiency":[17],"and":[18,41,62,88,162],"increase":[19],"applications.":[22],"However,":[23],"training":[24],"such":[25,100],"a":[26,29,91,165,171],"network":[27,168],"requires":[28],"large":[30],"annotated":[31,173],"dataset,":[32],"which":[33,55],"is":[34,90,153],"very":[35,155],"expensive":[36],"term":[38],"time":[40],"human":[42],"effort.":[43],"In":[44,104],"this":[45,105,133],"context,":[46],"several":[47],"automatic":[48,110],"image":[49,111,159],"annotation":[50,112],"solutions":[51],"been":[53],"proposed":[54,151],"are":[56,81],"dependent":[57],"on":[58],"rich":[59],"visual":[60,96],"features":[61],"thus":[63],"suitable":[64],"color":[66,78],"images":[67,119],"only.":[68],"On":[69],"other":[71,95],"hand,":[72],"recent":[73],"experiments":[74,138],"proven":[76],"that":[77,149],"cameras":[79,176],"alone":[80],"not":[82],"enough":[83],"pedestrian":[85,115],"related":[86],"applications":[87],"there":[89],"need":[92],"to":[93,126,157,163],"use":[94],"sensors":[97],"as":[98,101],"well,":[99],"thermal":[102,118],"cameras.":[103],"paper,":[106,134],"we":[107],"propose":[108],"an":[109,121],"technique":[113,152],"using":[120],"adaptive":[122],"background/foreground":[123],"estimation":[124],"model":[125],"train":[127,164],"Faster-RCNN.":[128],"The":[129,145],"results":[130,146],"presented":[131],"obtained":[135],"through":[136],"longterm":[137],"demonstrate":[139],"efficacy":[141],"our":[143,150],"technique.":[144],"also":[147],"show":[148],"be":[154],"useful":[156],"generate":[158],"annotations":[160],"automatically":[161],"deep":[166],"neural":[167],"without":[169],"having":[170],"manually":[172],"dataset":[174],"different":[178],"modalities.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2021-01-18T00:00:00"}