{"id":"https://openalex.org/W2952624574","doi":"https://doi.org/10.1109/ivs.2016.7535529","title":"Car type recognition with Deep Neural Networks","display_name":"Car type recognition with Deep Neural Networks","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2952624574","doi":"https://doi.org/10.1109/ivs.2016.7535529","mag":"2952624574"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2016.7535529","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2016.7535529","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1602.07125","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Heikki Huttunen","orcid":null},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]},{"id":"https://openalex.org/I4210133110","display_name":"Tampere University","ror":null,"country_code":"FI","type":null,"lineage":["https://openalex.org/I4210133110"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Heikki Huttunen","raw_affiliation_strings":["Tampere University of Technology, Finland","Visy Oy, Tampere, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tampere University of Technology, Finland","institution_ids":["https://openalex.org/I166825849","https://openalex.org/I4210133110"]},{"raw_affiliation_string":"Visy Oy, Tampere, Finland","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Fatemeh Shokrollahi Yancheshmeh","orcid":null},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]},{"id":"https://openalex.org/I4210133110","display_name":"Tampere University","ror":null,"country_code":"FI","type":null,"lineage":["https://openalex.org/I4210133110"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Fatemeh Shokrollahi Yancheshmeh","raw_affiliation_strings":["Tampere University of Technology, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tampere University of Technology, Finland","institution_ids":["https://openalex.org/I166825849","https://openalex.org/I4210133110"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ke Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]},{"id":"https://openalex.org/I4210133110","display_name":"Tampere University","ror":null,"country_code":"FI","type":null,"lineage":["https://openalex.org/I4210133110"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Ke Chen","raw_affiliation_strings":["Tampere University of Technology, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tampere University of Technology, Finland","institution_ids":["https://openalex.org/I166825849","https://openalex.org/I4210133110"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.5162,"has_fulltext":false,"cited_by_count":70,"citation_normalized_percentile":{"value":0.95445646,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1115","last_page":"1120"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9994000196456909,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.6988999843597412},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6256999969482422},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5532000064849854},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.534600019454956},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.5181000232696533},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4507000148296356}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.767300009727478},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6988999843597412},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6654000282287598},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6256999969482422},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5532000064849854},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.534600019454956},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.5181000232696533},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4507000148296356},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4065999984741211},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.3546999990940094},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33640000224113464},{"id":"https://openalex.org/C2777299769","wikidata":"https://www.wikidata.org/wiki/Q3707858","display_name":"Type (biology)","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2556000053882599},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.25360000133514404}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ivs.2016.7535529","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2016.7535529","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1602.07125","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1602.07125","pdf_url":"https://arxiv.org/pdf/1602.07125","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1602.07125","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1602.07125","pdf_url":"https://arxiv.org/pdf/1602.07125","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W238370718","https://openalex.org/W1522734439","https://openalex.org/W1958236864","https://openalex.org/W1976921161","https://openalex.org/W1977295328","https://openalex.org/W2029315852","https://openalex.org/W2104671752","https://openalex.org/W2124386111","https://openalex.org/W2138011018","https://openalex.org/W2144426297","https://openalex.org/W2145287260","https://openalex.org/W2145339207","https://openalex.org/W2155893237","https://openalex.org/W2162915993","https://openalex.org/W4239510810","https://openalex.org/W6622239757","https://openalex.org/W6636494156","https://openalex.org/W6674385629","https://openalex.org/W6674914833","https://openalex.org/W6684191040","https://openalex.org/W6997266731"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1,19],"paper":[2],"we":[3,21],"study":[4],"automatic":[5],"recognition":[6],"of":[7,9,41,49,67],"cars":[8],"four":[10],"types:":[11],"Bus,":[12],"Truck,":[13],"Van":[14],"and":[15,31,53],"Small":[16],"car.":[17],"For":[18],"problem":[20],"consider":[22],"two":[23],"data":[24],"driven":[25],"frameworks:":[26],"a":[27,32,47],"deep":[28],"neural":[29],"network":[30],"support":[33],"vector":[34],"machine":[35],"using":[36],"SIFT":[37],"features.":[38],"The":[39],"accuracy":[40,57],"the":[42,54,65],"methods":[43],"is":[44,58],"validated":[45],"with":[46],"database":[48],"over":[50,59],"6500":[51],"images,":[52],"resulting":[55],"prediction":[56],"97":[60],"%.":[61],"This":[62],"clearly":[63],"exceeds":[64],"accuracies":[66],"earlier":[68],"studies":[69],"that":[70],"use":[71],"manually":[72],"engineered":[73],"feature":[74],"extraction":[75],"pipelines.":[76]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":4}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2019-06-27T00:00:00"}
