{"id":"https://openalex.org/W4416470802","doi":"https://doi.org/10.1007/s00138-025-01764-y","title":"MVUDA: Unsupervised Domain Adaptation for Multi-view Pedestrian Detection","display_name":"MVUDA: Unsupervised Domain Adaptation for Multi-view Pedestrian Detection","publication_year":2025,"publication_date":"2025-11-21","ids":{"openalex":"https://openalex.org/W4416470802","doi":"https://doi.org/10.1007/s00138-025-01764-y"},"language":"en","primary_location":{"id":"doi:10.1007/s00138-025-01764-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00138-025-01764-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00138-025-01764-y.pdf","source":{"id":"https://openalex.org/S27728525","display_name":"Machine Vision and Applications","issn_l":"0932-8092","issn":["0932-8092","1432-1769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Vision and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00138-025-01764-y.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111142522","display_name":"Erik Brorsson","orcid":null},"institutions":[{"id":"https://openalex.org/I1340210623","display_name":"Volvo (Sweden)","ror":"https://ror.org/05b6ypc36","country_code":"SE","type":"company","lineage":["https://openalex.org/I1340210623"]},{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Erik Brorsson","raw_affiliation_strings":["Department of Electrical Engineering, Chalmers University of Technology, G\u00f6teborg, Sweden","Global Trucks Operations, Volvo Group, G\u00f6teborg, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Chalmers University of Technology, G\u00f6teborg, Sweden","institution_ids":["https://openalex.org/I66862912"]},{"raw_affiliation_string":"Global Trucks Operations, Volvo Group, G\u00f6teborg, Sweden","institution_ids":["https://openalex.org/I1340210623"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029413988","display_name":"Lennart Svensson","orcid":"https://orcid.org/0000-0003-0206-9186"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Lennart Svensson","raw_affiliation_strings":["Department of Electrical Engineering, Chalmers University of Technology, G\u00f6teborg, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Chalmers University of Technology, G\u00f6teborg, Sweden","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047696349","display_name":"Kristofer Bengtsson","orcid":"https://orcid.org/0000-0002-5290-682X"},"institutions":[{"id":"https://openalex.org/I1340210623","display_name":"Volvo (Sweden)","ror":"https://ror.org/05b6ypc36","country_code":"SE","type":"company","lineage":["https://openalex.org/I1340210623"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Kristofer Bengtsson","raw_affiliation_strings":["Global Trucks Operations, Volvo Group, G\u00f6teborg, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Global Trucks Operations, Volvo Group, G\u00f6teborg, Sweden","institution_ids":["https://openalex.org/I1340210623"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108805135","display_name":"Knut \u00c5kesson","orcid":"https://orcid.org/0000-0001-6105-2726"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Knut \u00c5kesson","raw_affiliation_strings":["Department of Electrical Engineering, Chalmers University of Technology, G\u00f6teborg, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Chalmers University of Technology, G\u00f6teborg, Sweden","institution_ids":["https://openalex.org/I66862912"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5111142522"],"corresponding_institution_ids":["https://openalex.org/I1340210623","https://openalex.org/I66862912"],"apc_list":{"value":2490,"currency":"EUR","value_usd":3090},"apc_paid":{"value":2490,"currency":"EUR","value_usd":3090},"fwci":1.0044,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.8250525,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"37","issue":"1","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":0.6377000212669373,"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":0.6377000212669373,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.12780000269412994,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.0754999965429306,"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":0.8715999722480774},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7936000227928162},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.7322999835014343},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.5662000179290771},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5515999794006348},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5435000061988831},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5412999987602234},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5078999996185303}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8715999722480774},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7936000227928162},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7483999729156494},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7322999835014343},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6965000033378601},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5662000179290771},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.554099977016449},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5435000061988831},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5412999987602234},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5078999996185303},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4936000108718872},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4433000087738037},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.42719998955726624},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.41609999537467957},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3671000003814697},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.35260000824928284},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3176000118255615},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C2777819797","wikidata":"https://www.wikidata.org/wiki/Q8010","display_name":"Pedestrian crossing","level":3,"score":0.29269999265670776},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.27140000462532043},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25949999690055847}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s00138-025-01764-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00138-025-01764-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00138-025-01764-y.pdf","source":{"id":"https://openalex.org/S27728525","display_name":"Machine Vision and Applications","issn_l":"0932-8092","issn":["0932-8092","1432-1769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Vision and Applications","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s00138-025-01764-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00138-025-01764-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00138-025-01764-y.pdf","source":{"id":"https://openalex.org/S27728525","display_name":"Machine Vision and Applications","issn_l":"0932-8092","issn":["0932-8092","1432-1769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Vision and Applications","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8869864247","display_name":null,"funder_award_id":"2022-06725","funder_id":"https://openalex.org/F4320322581","funder_display_name":"Vetenskapsr\u00e5det"}],"funders":[{"id":"https://openalex.org/F4320321523","display_name":"Chalmers Tekniska H\u00f6gskola","ror":"https://ror.org/040wg7k59"},{"id":"https://openalex.org/F4320322327","display_name":"Knut och Alice Wallenbergs Stiftelse","ror":"https://ror.org/004hzzk67"},{"id":"https://openalex.org/F4320322581","display_name":"Vetenskapsr\u00e5det","ror":"https://ror.org/03zttf063"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416470802.pdf","grobid_xml":"https://content.openalex.org/works/W4416470802.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1977352985","https://openalex.org/W1994774201","https://openalex.org/W2058536148","https://openalex.org/W2064142958","https://openalex.org/W2108598243","https://openalex.org/W2141822239","https://openalex.org/W2153492321","https://openalex.org/W2158634074","https://openalex.org/W2194775991","https://openalex.org/W2588287671","https://openalex.org/W2799108620","https://openalex.org/W2908322352","https://openalex.org/W2962687275","https://openalex.org/W2963107255","https://openalex.org/W2963587345","https://openalex.org/W2963789515","https://openalex.org/W2963859909","https://openalex.org/W2979548969","https://openalex.org/W3119635706","https://openalex.org/W3120048558","https://openalex.org/W3166409449","https://openalex.org/W3202890594","https://openalex.org/W3207737630","https://openalex.org/W3217147624","https://openalex.org/W4284884166","https://openalex.org/W4312417027","https://openalex.org/W4312993742","https://openalex.org/W4319300102","https://openalex.org/W4319336168","https://openalex.org/W4319336397","https://openalex.org/W4384519478","https://openalex.org/W4386071781","https://openalex.org/W4389925737","https://openalex.org/W4394625853","https://openalex.org/W4394862708","https://openalex.org/W4400871630","https://openalex.org/W4401833721","https://openalex.org/W4403535108"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"We":[1],"address":[2],"multi-view":[3,27,91,152],"pedestrian":[4,28,92,153],"detection":[5],"in":[6],"a":[7,15,45,85,157],"setting":[8],"where":[9],"labeled":[10,74,117,124],"data":[11],"is":[12],"collected":[13],"using":[14],"multi-camera":[16],"setup":[17],"different":[18,46],"from":[19],"the":[20,33,66,79,113,129,149],"one":[21],"used":[22,36],"for":[23,37,115,161],"testing.":[24],"While":[25],"recent":[26],"detectors":[29,154],"perform":[30],"well":[31],"on":[32,99,123],"camera":[34,54,144],"rig":[35],"training,":[38],"their":[39],"performance":[40,98],"declines":[41],"when":[42],"applied":[43],"to":[44,68,90],"setup.":[47],"To":[48],"facilitate":[49],"seamless":[50],"deployment":[51],"across":[52,143],"varied":[53],"rigs,":[55],"we":[56,77],"propose":[57],"an":[58],"unsupervised":[59],"domain":[60],"adaptation":[61,142],"(UDA)":[62],"method":[63,95,133],"that":[64],"adapts":[65],"model":[67],"new":[69],"rigs":[70],"without":[71],"requiring":[72],"additional":[73],"data.":[75,125],"Specifically,":[76],"leverage":[78],"mean":[80],"teacher":[81],"self-training":[82],"framework":[83],"with":[84],"novel":[86],"pseudo-labeling":[87],"technique":[88],"tailored":[89],"detection.":[93],"This":[94],"achieves":[96],"state-of-the-art":[97],"multiple":[100],"benchmarks,":[101],"including":[102],"MultiviewX":[103],"$$\\rightarrow":[104],"$$":[105],"Wildtrack.":[106],"Unlike":[107],"previous":[108],"methods,":[109],"our":[110,132,146],"approach":[111],"eliminates":[112],"need":[114],"external":[116],"monocular":[118],"datasets,":[119],"thereby":[120],"reducing":[121],"reliance":[122],"Extensive":[126],"evaluations":[127],"demonstrate":[128],"effectiveness":[130],"of":[131,151],"and":[134,155],"validate":[135],"key":[136],"design":[137],"choices.":[138],"By":[139],"enabling":[140],"robust":[141],"setups,":[145],"work":[147],"enhances":[148],"practicality":[150],"establishes":[156],"strong":[158],"UDA":[159],"baseline":[160],"future":[162],"research.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-11-23T00:00:00"}
