{"id":"https://openalex.org/W2026469559","doi":"https://doi.org/10.1109/wacv.2011.5711553","title":"Bayesian 3D model based human detection in crowded scenes using efficient optimization","display_name":"Bayesian 3D model based human detection in crowded scenes using efficient optimization","publication_year":2011,"publication_date":"2011-01-01","ids":{"openalex":"https://openalex.org/W2026469559","doi":"https://doi.org/10.1109/wacv.2011.5711553","mag":"2026469559"},"language":"en","primary_location":{"id":"doi:10.1109/wacv.2011.5711553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2011.5711553","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE Workshop on Applications of Computer Vision (WACV)","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/A5100364363","display_name":"Lu Wang","orcid":"https://orcid.org/0000-0001-5099-0522"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Lu Wang","raw_affiliation_strings":["Laboratory of Intelligent Transportation Systems, Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China","Laboratory of Intelligent Transportation Systems, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China"],"affiliations":[{"raw_affiliation_string":"Laboratory of Intelligent Transportation Systems, Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"Laboratory of Intelligent Transportation Systems, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111714815","display_name":"N.H.C. Yung","orcid":null},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Nelson H. C. Yung","raw_affiliation_strings":["Laboratory of Intelligent Transportation Systems, Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China","Laboratory of Intelligent Transportation Systems, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China"],"affiliations":[{"raw_affiliation_string":"Laboratory of Intelligent Transportation Systems, Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"Laboratory of Intelligent Transportation Systems, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100364363"],"corresponding_institution_ids":["https://openalex.org/I889458895"],"apc_list":null,"apc_paid":null,"fwci":0.7727,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74395922,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"46 a","issue":null,"first_page":"557","last_page":"563"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9995999932289124,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6919844150543213},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6228922009468079},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6150467395782471},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5954553484916687},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5154119729995728},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5059642195701599},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4689205288887024},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.42959365248680115},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.42434072494506836},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40717825293540955},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.262909471988678},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09076443314552307}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6919844150543213},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6228922009468079},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6150467395782471},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5954553484916687},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5154119729995728},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5059642195701599},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4689205288887024},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.42959365248680115},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.42434072494506836},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40717825293540955},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.262909471988678},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09076443314552307}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/wacv.2011.5711553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2011.5711553","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE Workshop on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},{"id":"pmh:oai:hub.hku.hk:10722/137725","is_oa":false,"landing_page_url":"http://hdl.handle.net/10722/137725","pdf_url":null,"source":{"id":"https://openalex.org/S4377196271","display_name":"The HKU Scholars Hub (University of Hong Kong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I889458895","host_organization_name":"University of Hong Kong","host_organization_lineage":["https://openalex.org/I889458895"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference_Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1590311443","https://openalex.org/W1591285480","https://openalex.org/W1991406901","https://openalex.org/W2072643367","https://openalex.org/W2097324787","https://openalex.org/W2100428290","https://openalex.org/W2101178587","https://openalex.org/W2111309546","https://openalex.org/W2118061983","https://openalex.org/W2119823327","https://openalex.org/W2122243179","https://openalex.org/W2133243643","https://openalex.org/W2135347708","https://openalex.org/W2161969291","https://openalex.org/W2587488405","https://openalex.org/W6635441548","https://openalex.org/W6674662834"],"related_works":["https://openalex.org/W3130468151","https://openalex.org/W4287752112","https://openalex.org/W3204329301","https://openalex.org/W2562263695","https://openalex.org/W2015518264","https://openalex.org/W2135187896","https://openalex.org/W2147201983","https://openalex.org/W2795035211","https://openalex.org/W2160108762","https://openalex.org/W1718066205"],"abstract_inverted_index":{"In":[0],"this":[1,79],"paper,":[2],"we":[3,87],"solve":[4],"the":[5,39,43,56,75,94,106,118,128,147,150,157,160,175,179,196,210,226],"problem":[6],"of":[7,42,105,152,228],"human":[8],"detection":[9],"in":[10,66,81,163],"crowded":[11],"scenes":[12],"using":[13],"a":[14,26,30,90,182,216],"Bayesian":[15],"3D":[16],"model":[17,72,133,180],"based":[18,187],"method.":[19],"Human":[20],"candidates":[21,44,59,197],"are":[22],"first":[23],"nominated":[24],"by":[25,54,138,171,220],"head":[27],"detector":[28],"and":[29,45,63,69,143,156,166,174,184,215],"foot":[31],"detector,":[32],"then":[33,70],"optimization":[34,108,120,130],"is":[35,52,101,110,115,125,136,169,192,206],"performed":[36,137],"to":[37,83,117,127,194],"find":[38],"best":[40],"configuration":[41],"their":[46],"corresponding":[47],"shape":[48,154],"models.":[49],"The":[50,103,203,222],"solution":[51],"obtained":[53],"decomposing":[55],"mutually":[57],"related":[58],"into":[60],"un-occluded":[61,76],"ones":[62,65],"occluded":[64],"each":[67],"iteration,":[68],"performing":[71],"matching":[73,201],"for":[74],"candidates.":[77],"To":[78],"end,":[80],"addition":[82],"some":[84],"obvious":[85],"clues,":[86],"also":[88],"derive":[89],"graph":[91],"that":[92,98,111,198],"depicts":[93],"inter-object":[95,161],"relation":[96],"so":[97],"unreasonable":[99],"decomposition":[100],"avoided.":[102],"merit":[104],"proposed":[107,204],"procedure":[109],"its":[112,123],"computational":[113],"cost":[114],"similar":[116],"greedy":[119],"methods":[121],"while":[122],"performance":[124],"comparable":[126],"global":[129],"approaches.":[131],"For":[132],"matching,":[134,181],"it":[135],"employing":[139],"both":[140,209],"prior":[141],"knowledge":[142],"image":[144,167],"likelihood,":[145],"where":[146],"priors":[148],"include":[149],"distribution":[151],"individual":[153],"models":[155],"restriction":[158],"on":[159,188,208],"distance":[162],"real":[164],"world,":[165],"likelihood":[168],"provided":[170],"foreground":[172],"extraction":[173],"edge":[176],"information.":[177],"After":[178],"validation":[183],"rejection":[185],"strategy":[186],"minimum":[189],"description":[190],"length":[191],"applied":[193],"confirm":[195],"have":[199],"reliable":[200],"results.":[202],"method":[205],"tested":[207],"publicly":[211],"available":[212],"Caviar":[213],"dataset":[214,218],"challenging":[217],"constructed":[219],"ourselves.":[221],"experimental":[223],"results":[224],"demonstrate":[225],"effectiveness":[227],"our":[229],"approach.":[230]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}