{"id":"https://openalex.org/W2128269617","doi":"https://doi.org/10.1109/wacv.2012.6163016","title":"Reconfigurable templates for robust vehicle detection and classification","display_name":"Reconfigurable templates for robust vehicle detection and classification","publication_year":2012,"publication_date":"2012-01-01","ids":{"openalex":"https://openalex.org/W2128269617","doi":"https://doi.org/10.1109/wacv.2012.6163016","mag":"2128269617"},"language":"en","primary_location":{"id":"doi:10.1109/wacv.2012.6163016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2012.6163016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Workshop on the Applications of Computer Vision (WACV)","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/A5064307909","display_name":"Yang Lv","orcid":"https://orcid.org/0000-0001-6888-4824"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Lv","raw_affiliation_strings":["Key Laboratory of Photoelectronic Imaging Technology and System, BIT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Photoelectronic Imaging Technology and System, BIT, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112692433","display_name":"Benjamin Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Benjamin Yao","raw_affiliation_strings":["Department of Statistics, UCLA, USA","Department of Statistics, UCLA, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, UCLA, USA","institution_ids":[]},{"raw_affiliation_string":"Department of Statistics, UCLA, USA#TAB#","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026250850","display_name":"Yongtian Wang","orcid":"https://orcid.org/0000-0001-9422-0888"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yongtian Wang","raw_affiliation_strings":["Key Laboratory of Photoelectronic Imaging Technology and System, BIT, China","Key Laboratory of Photoelectronic Imaging Technology and System, BIT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Photoelectronic Imaging Technology and System, BIT, China","institution_ids":[]},{"raw_affiliation_string":"Key Laboratory of Photoelectronic Imaging Technology and System, BIT, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034228010","display_name":"Song\u2010Chun Zhu","orcid":"https://orcid.org/0000-0002-1925-5973"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Song-Chun Zhu","raw_affiliation_strings":["Department of Statistics, UCLA, USA","Department of Statistics, UCLA, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, UCLA, USA","institution_ids":[]},{"raw_affiliation_string":"Department of Statistics, UCLA, USA#TAB#","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":null,"first_page":"321","last_page":"328"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9975000023841858,"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":0.996999979019165,"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/template","display_name":"Template","score":0.717901349067688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6624634265899658},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40873926877975464},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.08674809336662292}],"concepts":[{"id":"https://openalex.org/C82714645","wikidata":"https://www.wikidata.org/wiki/Q438331","display_name":"Template","level":2,"score":0.717901349067688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6624634265899658},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40873926877975464},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.08674809336662292}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv.2012.6163016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2012.6163016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Workshop on the Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5699999928474426}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1561099028","https://openalex.org/W1573508639","https://openalex.org/W1821057786","https://openalex.org/W1988790447","https://openalex.org/W2030536784","https://openalex.org/W2036989445","https://openalex.org/W2039050532","https://openalex.org/W2051800765","https://openalex.org/W2079930597","https://openalex.org/W2108890589","https://openalex.org/W2126747264","https://openalex.org/W2145743319","https://openalex.org/W2151693816","https://openalex.org/W2152826865","https://openalex.org/W2154422044","https://openalex.org/W2161969291","https://openalex.org/W2168356304","https://openalex.org/W2169696215","https://openalex.org/W3098888484","https://openalex.org/W4233761317","https://openalex.org/W6633857474","https://openalex.org/W6634262155","https://openalex.org/W6676020639","https://openalex.org/W6678637433"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2121300814","https://openalex.org/W4231091074","https://openalex.org/W1886613375","https://openalex.org/W4236081792","https://openalex.org/W4250583430","https://openalex.org/W4234406076","https://openalex.org/W2010731026","https://openalex.org/W4311328601"],"abstract_inverted_index":{"In":[0,70,171],"this":[1,112,145],"paper,":[2],"we":[3,88,122,174],"learn":[4,45,115],"a":[5,17,56,90,106,139,153,183],"reconfigurable":[6,42],"template":[7,29,43],"for":[8,20,55],"detecting":[9],"vehicles":[10],"and":[11,34,78,99,182,190,207],"classifying":[12],"their":[13],"types.":[14],"We":[15,143],"adopt":[16],"popular":[18],"design":[19,89],"the":[21,50,75,116,126,132,149,172,186],"part":[22,46,58],"based":[23,59],"model":[24,209],"that":[25,48,130,157,192],"has":[26],"one":[27],"coarse":[28],"covering":[30],"entire":[31],"object":[32],"window":[33],"several":[35],"small":[36],"high-resolution":[37],"templates":[38,64],"representing":[39],"parts.":[40,170],"The":[41,61],"can":[44,158],"configurations":[47],"capture":[49],"spatial":[51,86],"correlation":[52],"of":[53,63,67,80,92,95,108,128,168,185],"features":[54,62,83],"deformable":[57],"model.":[60],"are":[65],"Histograms":[66],"Gradients":[68],"(HoG).":[69],"order":[71],"to":[72,124,138],"better":[73],"describe":[74],"actual":[76],"dimensions":[77],"locations":[79],"\u201cparts\u201d":[81],"(i.e.":[82],"with":[84,152,179,195],"strong":[85],"correlations),":[87],"dictionary":[91],"rectangular":[93],"primitives":[94,110],"various":[96],"sizes,":[97],"aspect-ratios":[98],"positions.":[100],"A":[101],"configuration":[102,118],"is":[103],"defined":[104],"as":[105],"subset":[107,127],"non-overlapping":[109],"from":[111],"dictionary.":[113],"To":[114],"optimal":[117],"using":[119],"SVM":[120],"amounts,":[121],"need":[123],"find":[125],"parts":[129],"minimize":[131],"regularized":[133],"hinge":[134,150],"loss,":[135],"which":[136],"leads":[137],"non-convex":[140],"optimization":[141],"problem.":[142],"solve":[144],"problem":[146],"by":[147],"replacing":[148],"loss":[151,156],"negative":[154,165],"sigmoid":[155,166],"be":[159],"approximately":[160],"decomposed":[161],"into":[162],"losses":[163],"(or":[164],"scores)":[167],"individual":[169],"experiment,":[173],"compare":[175],"our":[176,196],"method":[177,188,197],"empirically":[178],"group":[180],"lasso":[181],"state":[184],"art":[187],"[7]":[189],"demonstrate":[191],"models":[193],"learned":[194],"outperform":[198],"others":[199],"on":[200],"two":[201],"computer":[202],"vision":[203],"applications:":[204],"vehicle":[205,208],"localization":[206],"recognition.":[210]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}