{"id":"https://openalex.org/W1999426038","doi":"https://doi.org/10.1109/iccvw.2009.5457647","title":"Transfer pedestrian detector towards view-adaptiveness and efficiency","display_name":"Transfer pedestrian detector towards view-adaptiveness and efficiency","publication_year":2009,"publication_date":"2009-09-01","ids":{"openalex":"https://openalex.org/W1999426038","doi":"https://doi.org/10.1109/iccvw.2009.5457647","mag":"1999426038"},"language":"en","primary_location":{"id":"doi:10.1109/iccvw.2009.5457647","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2009.5457647","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops","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/A5020694008","display_name":"Junbiao Pang","orcid":"https://orcid.org/0000-0001-8153-7229"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junbiao Pang","raw_affiliation_strings":["Chinese Academy and Sciences, China","Key Lab. of Intell. Info. Process, Inst, of Comput. Tech., Chinese Academy of Sciences, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy and Sciences, China","institution_ids":[]},{"raw_affiliation_string":"Key Lab. of Intell. Info. Process, Inst, of Comput. Tech., Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028597017","display_name":"Qingming Huang","orcid":"https://orcid.org/0000-0001-7542-296X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingming Huang","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, Beijing, CN","Key Lab. of Intell. Info. Process, Inst, of Comput. Tech., Chinese Academy of Sciences, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, Beijing, CN","institution_ids":["https://openalex.org/I19820366"]},{"raw_affiliation_string":"Key Lab. of Intell. Info. Process, Inst, of Comput. Tech., Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085719285","display_name":"Shuqiang Jiang","orcid":"https://orcid.org/0000-0002-1596-4326"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210133325","display_name":"Institute of Process Engineering","ror":"https://ror.org/03j4x9j18","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210133325"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuqiang Jiang","raw_affiliation_strings":["Key Lab, Intell Info Process, Institute of Computer Technology, Chinese Academy and Sciences, China","Key Lab. of Intell. Info. Process, Inst, of Comput. Tech., Chinese Academy of Sciences, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Lab, Intell Info Process, Institute of Computer Technology, Chinese Academy and Sciences, China","institution_ids":["https://openalex.org/I4210133325"]},{"raw_affiliation_string":"Key Lab. of Intell. Info. Process, Inst, of Comput. Tech., Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102726925","display_name":"Zhipeng Wu","orcid":"https://orcid.org/0000-0002-7627-1099"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhipeng Wu","raw_affiliation_strings":["Chinese Academy and Sciences, China","Graduate University of Chinese Academy of Sciences, China#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy and Sciences, China","institution_ids":[]},{"raw_affiliation_string":"Graduate University of Chinese Academy of Sciences, China#TAB#","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2682,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.41805378,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":null,"first_page":"609","last_page":"616"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"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":0.9940000176429749,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.8487557172775269},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.7765109539031982},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7618652582168579},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.7496014833450317},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7203384637832642},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5404689311981201},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.5357465147972107},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.5072631239891052},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5066097974777222},{"id":"https://openalex.org/keywords/notation","display_name":"Notation","score":0.4425014555454254},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.42924246191978455},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12245932221412659},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10069528222084045}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.8487557172775269},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.7765109539031982},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7618652582168579},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7496014833450317},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7203384637832642},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5404689311981201},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.5357465147972107},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.5072631239891052},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5066097974777222},{"id":"https://openalex.org/C45357846","wikidata":"https://www.wikidata.org/wiki/Q2001982","display_name":"Notation","level":2,"score":0.4425014555454254},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.42924246191978455},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12245932221412659},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10069528222084045},{"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/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","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":1,"locations":[{"id":"doi:10.1109/iccvw.2009.5457647","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2009.5457647","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W189742998","https://openalex.org/W621727291","https://openalex.org/W1487322600","https://openalex.org/W1502609557","https://openalex.org/W1510488293","https://openalex.org/W1607198972","https://openalex.org/W1988790447","https://openalex.org/W2036043322","https://openalex.org/W2048679005","https://openalex.org/W2103577141","https://openalex.org/W2109258005","https://openalex.org/W2113201641","https://openalex.org/W2115733720","https://openalex.org/W2115763357","https://openalex.org/W2120219904","https://openalex.org/W2121680631","https://openalex.org/W2122838776","https://openalex.org/W2130903752","https://openalex.org/W2133013156","https://openalex.org/W2148696156","https://openalex.org/W2161406034","https://openalex.org/W2164598857","https://openalex.org/W2167090521","https://openalex.org/W2169743339","https://openalex.org/W2170858806","https://openalex.org/W2171188998","https://openalex.org/W2400267228","https://openalex.org/W2913340405","https://openalex.org/W3035219538","https://openalex.org/W4230946174","https://openalex.org/W4248437541","https://openalex.org/W4285719527","https://openalex.org/W6629032133","https://openalex.org/W6630349879","https://openalex.org/W6631781440","https://openalex.org/W6662528715","https://openalex.org/W6676156825","https://openalex.org/W6679564466","https://openalex.org/W6679734692","https://openalex.org/W6685004768","https://openalex.org/W6780052409","https://openalex.org/W7062423369"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W2985746494","https://openalex.org/W4206042385","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2116862786","https://openalex.org/W2914343065","https://openalex.org/W2751005898"],"abstract_inverted_index":{"The":[0,57],"distribution":[1],"disparity":[2],"is":[3,60],"often":[4],"inevitable":[5],"between":[6],"the":[7,12,63,68,85,89,92,106,113,123,133],"pedestrian":[8],"training":[9],"examples":[10,54],"and":[11,88,103,110,112],"test":[13],"data":[14],"from":[15],"a":[16,36,41,46,72,96],"specific":[17,76],"application":[18],"scenario,":[19,43,98],"which":[20,121],"may":[21],"result":[22],"in":[23],"unsatisfactory":[24],"detection":[25,135],"accuracies.":[26],"In":[27],"this":[28,80],"paper,":[29],"we":[30],"investigate":[31],"how":[32],"to":[33,61,71,77,105],"efficiently":[34],"adapt":[35],"generic":[37],"boosting-style":[38,78],"detector":[39,70,130],"for":[40,91],"new":[42,73,97,146],"e.g.,":[44],"with":[45,50,143],"distinctive":[47],"capture":[48],"view-angle,":[49],"only":[51],"very":[52,144],"limited":[53,145],"(e.g.,":[55],"~200).":[56],"basic":[58],"notation":[59],"transfer":[62],"auxiliary":[64,81],"knowledge":[65,83],"encoded":[66],"within":[67],"well-trained":[69],"scenario.":[74],"When":[75],"detectors,":[79],"prior":[82],"includes":[84],"selected":[86],"features":[87,100],"weights":[90,114],"weak":[93],"classifiers.":[94],"For":[95],"these":[99],"are":[101,115],"reused":[102],"shifted":[104],"most":[107],"discriminative":[108],"positions":[109],"scales,":[111],"further":[116],"adapted":[117],"by":[118,139],"covariate":[119,124],"shift,":[120],"introduces":[122],"loss.":[125],"Extensive":[126],"experiments":[127],"on":[128],"cross-view":[129],"adaption":[131],"show":[132],"encouraging":[134],"accuracy":[136],"improvements":[137],"brought":[138],"our":[140],"proposed":[141],"algorithm":[142],"examples.":[147]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}