{"id":"https://openalex.org/W2540816793","doi":"https://doi.org/10.1109/acpr.2011.6166674","title":"Human centric object detection in highly crowded scenes","display_name":"Human centric object detection in highly crowded scenes","publication_year":2011,"publication_date":"2011-11-01","ids":{"openalex":"https://openalex.org/W2540816793","doi":"https://doi.org/10.1109/acpr.2011.6166674","mag":"2540816793"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2011.6166674","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2011.6166674","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The First Asian Conference on Pattern Recognition","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/A5069092263","display_name":"Genquan Duan","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Genquan Duan","raw_affiliation_strings":["Computer Science and Technology Department, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Computer Science and Technology Department, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074577884","display_name":"Haizhou Ai","orcid":"https://orcid.org/0000-0002-0166-5755"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haizhou Ai","raw_affiliation_strings":["Computer Science and Technology Department, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Computer Science and Technology Department, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062009349","display_name":"Takayoshi Yamashita","orcid":"https://orcid.org/0000-0003-2631-9856"},"institutions":[{"id":"https://openalex.org/I146230289","display_name":"Omron (Japan)","ror":"https://ror.org/00q0w1h45","country_code":"JP","type":"company","lineage":["https://openalex.org/I146230289"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takayoshi Yamashita","raw_affiliation_strings":["OMRON Corporation, Shiga, Japan"],"affiliations":[{"raw_affiliation_string":"OMRON Corporation, Shiga, Japan","institution_ids":["https://openalex.org/I146230289"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110512343","display_name":"Shihong Lao","orcid":null},"institutions":[{"id":"https://openalex.org/I146230289","display_name":"Omron (Japan)","ror":"https://ror.org/00q0w1h45","country_code":"JP","type":"company","lineage":["https://openalex.org/I146230289"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shihong Lao","raw_affiliation_strings":["OMRON Social Solutions Company Limited, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"OMRON Social Solutions Company Limited, Kyoto, Japan","institution_ids":["https://openalex.org/I146230289"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5069092263"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33646035,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"100","last_page":"104"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9984999895095825,"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":0.9944999814033508,"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":0.8096361756324768},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7555936574935913},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7122188806533813},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6872660517692566},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5183411836624146},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4763734042644501},{"id":"https://openalex.org/keywords/spatial-relation","display_name":"Spatial relation","score":0.473127543926239},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46050024032592773},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4121930003166199},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.376687616109848}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8096361756324768},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7555936574935913},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7122188806533813},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6872660517692566},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5183411836624146},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4763734042644501},{"id":"https://openalex.org/C27511587","wikidata":"https://www.wikidata.org/wiki/Q2178623","display_name":"Spatial relation","level":2,"score":0.473127543926239},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46050024032592773},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4121930003166199},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.376687616109848}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/acpr.2011.6166674","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2011.6166674","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The First Asian Conference on Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.294.6278","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.294.6278","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://media.cs.tsinghua.edu.cn/~imagevision/papers/[2011][acpr]duan genquan.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1522947131","https://openalex.org/W1547445461","https://openalex.org/W2022897019","https://openalex.org/W2024046085","https://openalex.org/W2046589395","https://openalex.org/W2083342768","https://openalex.org/W2097324787","https://openalex.org/W2098699644","https://openalex.org/W2120419212","https://openalex.org/W2127791185","https://openalex.org/W2157890879","https://openalex.org/W2161969291","https://openalex.org/W2168108030","https://openalex.org/W2171188998","https://openalex.org/W2534262995","https://openalex.org/W6631334663","https://openalex.org/W6655798865","https://openalex.org/W6684531988"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W2369710579","https://openalex.org/W4327728159","https://openalex.org/W4394266730","https://openalex.org/W1990856605","https://openalex.org/W2053783616","https://openalex.org/W2971551846","https://openalex.org/W1545923090"],"abstract_inverted_index":{"In":[0,35],"this":[1],"paper,":[2],"we":[3,82],"propose":[4],"to":[5,28,40,50,64,73,108,139],"detect":[6,41,74],"human":[7],"centric":[8],"objects,":[9],"including":[10],"face,":[11],"head":[12],"shoulder,":[13],"upper":[14],"body,":[15,17,22],"left":[16],"right":[18],"body":[19],"and":[20,52,72,90,95,120,148],"whole":[21,63],"which":[23,131],"can":[24,167],"provide":[25],"essential":[26],"information":[27],"locate":[29],"humans":[30],"in":[31,130],"highly":[32],"crowed":[33],"scenes.":[34],"the":[36,38,88,96,99],"literature,":[37],"approaches":[39],"multi-class":[42],"objects":[43],"are":[44,150],"either":[45],"taking":[46,58],"each":[47],"class":[48,110],"independently":[49],"learn":[51,65],"apply":[53],"its":[54],"classifier":[55,67],"successively":[56],"or":[57],"all":[59],"classes":[60,94,129,143],"as":[61],"a":[62,114,132],"individual":[66],"based":[68],"on":[69,158],"sharing":[70],"features":[71],"by":[75],"step-by-step":[76],"dividing.":[77],"Different":[78],"from":[79],"these":[80,153],"works,":[81],"consider":[83],"two":[84,154],"issues,":[85],"one":[86],"is":[87,98,107,137],"similarities":[89],"discriminations":[91],"of":[92,127],"different":[93],"other":[97],"semantic":[100],"relations":[101,141],"among":[102,142],"them.":[103],"Our":[104],"main":[105],"idea":[106],"predict":[109],"labels":[111],"quickly":[112],"using":[113,125],"Salient":[115],"Patch":[116],"Model":[117,135],"(SPM)":[118],"first,":[119],"then":[121],"do":[122],"detection":[123],"accurately":[124],"detectors":[126],"predicted":[128],"Semantic":[133],"Relation":[134],"(SRM)":[136],"proposed":[138,165],"capture":[140],"for":[144,152],"efficient":[145],"inferences.":[146],"SPM":[147],"SRM":[149],"designed":[151],"issues":[155],"respectively.":[156],"Experiments":[157],"challenging":[159],"real-world":[160],"datasets":[161],"demonstrate":[162],"that":[163],"our":[164],"approach":[166],"achieve":[168],"significant":[169],"performance":[170],"improvements.":[171]},"counts_by_year":[],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}