{"id":"https://openalex.org/W2048628013","doi":"https://doi.org/10.1109/ivs.2012.6232185","title":"An evaluation of boosted features for vehicle detection","display_name":"An evaluation of boosted features for vehicle detection","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2048628013","doi":"https://doi.org/10.1109/ivs.2012.6232185","mag":"2048628013"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2012.6232185","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2012.6232185","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Intelligent Vehicles Symposium","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/A5060372724","display_name":"Liwei Liu","orcid":"https://orcid.org/0000-0001-5884-7333"},"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":"Liwei Liu","raw_affiliation_strings":["Computer Science and Technology Department, Tsinghua University, Beijing, China","Computer Science and Technology Dept., Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Technology Department, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Computer Science and Technology Dept., Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","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":false,"raw_author_name":"Genquan Duan","raw_affiliation_strings":["Computer Science and Technology Department, Tsinghua University, Beijing, China","Computer Science and Technology Dept., Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Technology Department, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Computer Science and Technology Dept., 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","Computer Science and Technology Dept., Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Technology Department, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Computer Science and Technology Dept., Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"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","OMRON Social Solutions Co., LTD, Kyoto 619-0283, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"OMRON Social Solutions Company Limited, Kyoto, Japan","institution_ids":["https://openalex.org/I146230289"]},{"raw_affiliation_string":"OMRON Social Solutions Co., LTD, Kyoto 619-0283, Japan","institution_ids":["https://openalex.org/I146230289"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4512,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60331324,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"956","last_page":"961"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T10036","display_name":"Advanced Neural Network Applications","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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9997000098228455,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.998199999332428,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8138084411621094},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.6415765285491943},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6233206391334534},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.611564576625824},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5942323803901672},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5355994701385498},{"id":"https://openalex.org/keywords/face-detection","display_name":"Face detection","score":0.5006897449493408},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.482255756855011},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.45829567313194275},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4492276906967163},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.44061779975891113},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.4327929615974426},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.42648929357528687},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4233688712120056},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3569951057434082},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3479011654853821},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.3288431763648987},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.20220232009887695},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.18981274962425232},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.1421891748905182},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11582556366920471},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.0924575924873352},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.0844375491142273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8138084411621094},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.6415765285491943},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6233206391334534},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.611564576625824},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5942323803901672},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5355994701385498},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.5006897449493408},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.482255756855011},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45829567313194275},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4492276906967163},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.44061779975891113},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.4327929615974426},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.42648929357528687},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4233688712120056},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3569951057434082},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3479011654853821},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.3288431763648987},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.20220232009887695},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.18981274962425232},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.1421891748905182},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11582556366920471},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0924575924873352},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0844375491142273},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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":2,"locations":[{"id":"doi:10.1109/ivs.2012.6232185","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2012.6232185","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Intelligent Vehicles Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.648.6678","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.648.6678","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/[2012]ivs2012-liu.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.4099999964237213,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1514449048","https://openalex.org/W1863635071","https://openalex.org/W1987118352","https://openalex.org/W1989192414","https://openalex.org/W2022897019","https://openalex.org/W2032210760","https://openalex.org/W2096883237","https://openalex.org/W2098999879","https://openalex.org/W2107775979","https://openalex.org/W2120419212","https://openalex.org/W2122868496","https://openalex.org/W2135931458","https://openalex.org/W2140677395","https://openalex.org/W2141659202","https://openalex.org/W2158756768","https://openalex.org/W2161969291","https://openalex.org/W2169696215","https://openalex.org/W2537125158","https://openalex.org/W3097096317","https://openalex.org/W4244104295","https://openalex.org/W4244952642","https://openalex.org/W4247356340","https://openalex.org/W6637369200","https://openalex.org/W6647375158","https://openalex.org/W6655798865","https://openalex.org/W6674719894","https://openalex.org/W6681010617"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2392615019","https://openalex.org/W2879046444","https://openalex.org/W2096042813","https://openalex.org/W2047758967","https://openalex.org/W2294130405","https://openalex.org/W2242267494","https://openalex.org/W2116589437","https://openalex.org/W2392583168"],"abstract_inverted_index":{"Vehicle":[0],"detection":[1,34,70,86],"in":[2,36,103,140,172],"traffic":[3,21],"scenes":[4],"is":[5],"a":[6,97,152,163,196],"fundamental":[7],"task":[8],"for":[9,68,185],"intelligent":[10,23],"transportation":[11],"system":[12],"and":[13,25,58,71,112,125,127,130,151,166,194,199,205],"has":[14,39],"many":[15,84],"practical":[16,213],"applications":[17],"as":[18,20,52],"diverse":[19],"monitoring,":[22],"scheduling":[24],"autonomous":[26],"navigation.":[27],"In":[28],"recent":[29],"years,":[30],"the":[31,145,156,180],"number":[32],"of":[33,44,62,83,100,137,169,182],"approaches":[35,87],"monocular":[37],"images":[38],"grown":[40],"rapidly.":[41],"However,":[42],"most":[43],"them":[45],"focus":[46],"on":[47,88,144],"detecting":[48],"other":[49],"objects":[50],"(such":[51],"face,":[53],"pedestrian,":[54],"cat,":[55],"dog,":[56],"etc.)":[57],"also":[59],"there":[60],"lacks":[61],"vehicle":[63,69,173,186],"datasets":[64],"with":[65,106],"various":[66],"conditions":[67,121],"comprehensive":[72],"comparisons.":[73],"To":[74],"address":[75],"these":[76,170,176,192],"problems,":[77],"we":[78,95,133,178,190],"perform":[79],"an":[80],"extensive":[81],"evaluation":[82,158],"state-of-the-art":[85,138],"vehicles.":[89],"Our":[90,160],"main":[91],"contributions":[92],"are:":[93],"(1)":[94],"collect":[96],"large":[98],"dataset":[99,147,154],"real-world":[101],"vehicles":[102],"frontal/rear":[104],"view":[105],"30\u00b0":[107],"~":[108,114],"-30\u00b0":[109],"yaw":[110],"changes":[111,117],"5\u00b0":[113],"45\u00b0":[115],"pitch":[116],"under":[118],"different":[119],"weather":[120],"(snowy,":[122],"rainy,":[123],"sunny":[124],"cloudy)":[126],"illumination":[128],"variations,":[129],"then":[131],"(2)":[132],"evaluate":[134],"six":[135],"types":[136],"features":[139,171,184],"Real":[141],"AdaBoost":[142],"framework":[143],"adequate":[146],"collected":[148],"by":[149],"ourselves":[150],"public":[153],"using":[155],"same":[157],"protocol.":[159],"study":[161],"presents":[162],"fair":[164],"comparison":[165],"deep":[167],"analysis":[168],"detection.":[174,187],"From":[175],"experiments,":[177],"explore":[179],"characteristics":[181,193],"good":[183],"(3)":[188],"Finally,":[189],"exploit":[191],"propose":[195],"relatively":[197],"effective":[198],"efficient":[200],"detector,":[201],"balancing":[202],"performance,":[203],"speed":[204],"memory":[206],"cost":[207],"which":[208],"can":[209],"be":[210],"put":[211],"into":[212],"use.":[214]},"counts_by_year":[{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}