{"id":"https://openalex.org/W2794958240","doi":"https://doi.org/10.1109/icce.2018.8326252","title":"Methodology for improving detection speed of pedestrians in autonomous vehicle by image class classification","display_name":"Methodology for improving detection speed of pedestrians in autonomous vehicle by image class classification","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2794958240","doi":"https://doi.org/10.1109/icce.2018.8326252","mag":"2794958240"},"language":"en","primary_location":{"id":"doi:10.1109/icce.2018.8326252","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce.2018.8326252","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Consumer Electronics (ICCE)","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/A5037859053","display_name":"Junkwang Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I193352282","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69","country_code":"KR","type":"education","lineage":["https://openalex.org/I193352282"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Junkwang Kim","raw_affiliation_strings":["Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea","institution_ids":["https://openalex.org/I193352282"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102128481","display_name":"Woo Young Jung","orcid":null},"institutions":[{"id":"https://openalex.org/I193352282","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69","country_code":"KR","type":"education","lineage":["https://openalex.org/I193352282"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Woo Young Jung","raw_affiliation_strings":["Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea","institution_ids":["https://openalex.org/I193352282"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064276223","display_name":"Heechul Jung","orcid":"https://orcid.org/0000-0002-3005-2560"},"institutions":[{"id":"https://openalex.org/I193352282","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69","country_code":"KR","type":"education","lineage":["https://openalex.org/I193352282"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Heechul Jung","raw_affiliation_strings":["Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea","institution_ids":["https://openalex.org/I193352282"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086165395","display_name":"Dong Seog Han","orcid":"https://orcid.org/0000-0002-7769-0236"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dong Seog Han","raw_affiliation_strings":["School of Electronics Engineering, Kyungpook National University, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics Engineering, Kyungpook National University, Republic of Korea","institution_ids":["https://openalex.org/I31419693"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.106,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.42433372,"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":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9986000061035156,"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":0.9986000061035156,"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":0.9986000061035156,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9958000183105469,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.8383266925811768},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.7322993278503418},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.716944694519043},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6371679306030273},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6095357537269592},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.5688661336898804},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5601462125778198},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.5302566885948181},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.530009388923645},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5117747187614441},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.46491390466690063},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4611208140850067},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4205614924430847},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4189378619194031},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33384057879447937},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.16017800569534302},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13179349899291992}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8383266925811768},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.7322993278503418},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.716944694519043},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6371679306030273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6095357537269592},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.5688661336898804},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5601462125778198},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.5302566885948181},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.530009388923645},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5117747187614441},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.46491390466690063},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4611208140850067},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4205614924430847},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4189378619194031},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33384057879447937},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.16017800569534302},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13179349899291992},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce.2018.8326252","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce.2018.8326252","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Consumer Electronics (ICCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1903127635","https://openalex.org/W1999133685","https://openalex.org/W2031454541","https://openalex.org/W2151103935","https://openalex.org/W2161969291","https://openalex.org/W2164598857","https://openalex.org/W2183182206","https://openalex.org/W2200528286","https://openalex.org/W2613718673","https://openalex.org/W2964137095"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2802018156","https://openalex.org/W4313315626","https://openalex.org/W2101531944","https://openalex.org/W2922437833","https://openalex.org/W2100052226","https://openalex.org/W4312696271","https://openalex.org/W4223892596","https://openalex.org/W2933098581"],"abstract_inverted_index":{"We":[0],"propose":[1,85],"a":[2,28,35,86],"pedestrian":[3,41,58],"detection":[4,17,42],"method":[5],"to":[6,51,68,78],"minimize":[7],"the":[8,24,44,63,98,105],"amount":[9],"of":[10,23,46],"computation":[11,65],"for":[12,31,89],"classifying":[13],"and":[14,93],"candidate":[15,47,73],"region":[16],"in":[18,59],"autonomous":[19],"vehicles.":[20],"The":[21],"minimization":[22],"computational":[25,37],"complexity":[26],"is":[27,49,56,66],"crucial":[29],"factor":[30],"commercial":[32],"products":[33],"with":[34],"limited":[36],"power.":[38],"In":[39,81],"conventional":[40],"methods,":[43],"number":[45],"regions":[48],"300":[50],"2,000":[52],"even":[53],"if":[54],"there":[55],"no":[57],"an":[60],"image.":[61],"Therefore,":[62],"unnecessary":[64],"significant":[67],"classify":[69],"each":[70],"falsely":[71],"decided":[72],"region.":[74],"Moreover,":[75],"it":[76],"leads":[77],"false":[79],"detection.":[80],"this":[82,91],"paper,":[83],"we":[84],"new":[87],"methodology":[88],"solving":[90],"problem,":[92],"show":[94],"through":[95],"experiments":[96],"that":[97],"processing":[99],"speed":[100],"can":[101],"be":[102],"improved":[103],"by":[104],"proposed":[106],"methodology.":[107]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}