{"id":"https://openalex.org/W2969808173","doi":"https://doi.org/10.1109/access.2019.2936454","title":"A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters","display_name":"A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2969808173","doi":"https://doi.org/10.1109/access.2019.2936454","mag":"2969808173"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2936454","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2936454","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08807116.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08807116.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101455685","display_name":"Yu Jiang","orcid":"https://orcid.org/0000-0002-1184-8037"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Jiang","raw_affiliation_strings":["Shanghai Key Laboratory of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-1184-8037","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012734224","display_name":"Guoxiang Tong","orcid":"https://orcid.org/0000-0003-3020-8278"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoxiang Tong","raw_affiliation_strings":["Shanghai Key Laboratory of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000265000","display_name":"Henan Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Henan Yin","raw_affiliation_strings":["Shanghai Key Laboratory of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103216531","display_name":"Naixue Xiong","orcid":"https://orcid.org/0000-0002-0394-4635"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Naixue Xiong","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-0394-4635","affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":9.032,"has_fulltext":true,"cited_by_count":207,"citation_normalized_percentile":{"value":0.98299574,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"7","issue":null,"first_page":"118310","last_page":"118321"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991999864578247,"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":0.9991999864578247,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9926999807357788,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.8390215635299683},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7686541676521301},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6541451215744019},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6097604036331177},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5731780529022217},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5530197024345398},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5202154517173767},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.4908181130886078},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.44031837582588196},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35224902629852295},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32721424102783203},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32684653997421265},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08239176869392395}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8390215635299683},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7686541676521301},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6541451215744019},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6097604036331177},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5731780529022217},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5530197024345398},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5202154517173767},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.4908181130886078},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.44031837582588196},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35224902629852295},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32721424102783203},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32684653997421265},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08239176869392395},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2936454","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2936454","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08807116.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7cdbe2121f6a43d7a81a4b398bb371ec","is_oa":true,"landing_page_url":"https://doaj.org/article/7cdbe2121f6a43d7a81a4b398bb371ec","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 118310-118321 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2936454","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2936454","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08807116.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G3725427234","display_name":null,"funder_award_id":"2018YFB1700902","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2969808173.pdf","grobid_xml":"https://content.openalex.org/works/W2969808173.grobid-xml"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W33554911","https://openalex.org/W1448296956","https://openalex.org/W1480376833","https://openalex.org/W1554825167","https://openalex.org/W1678356000","https://openalex.org/W1687797484","https://openalex.org/W1873421284","https://openalex.org/W1971260286","https://openalex.org/W1985258161","https://openalex.org/W1986905809","https://openalex.org/W2005489441","https://openalex.org/W2007624296","https://openalex.org/W2014240407","https://openalex.org/W2031454541","https://openalex.org/W2033625293","https://openalex.org/W2034779469","https://openalex.org/W2067191022","https://openalex.org/W2070644787","https://openalex.org/W2107775979","https://openalex.org/W2121969814","https://openalex.org/W2123471354","https://openalex.org/W2125556102","https://openalex.org/W2133984628","https://openalex.org/W2149298154","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2164475608","https://openalex.org/W2166623283","https://openalex.org/W2168356304","https://openalex.org/W2191835017","https://openalex.org/W2200528286","https://openalex.org/W2205255364","https://openalex.org/W2242014407","https://openalex.org/W2294734639","https://openalex.org/W2295598076","https://openalex.org/W2354440614","https://openalex.org/W2401178140","https://openalex.org/W2548197316","https://openalex.org/W2586297576","https://openalex.org/W2766447205","https://openalex.org/W2786685435","https://openalex.org/W2849544032","https://openalex.org/W2860585329","https://openalex.org/W2904792970","https://openalex.org/W2908294476","https://openalex.org/W2911381491","https://openalex.org/W2945427015","https://openalex.org/W3097096317","https://openalex.org/W3098217967","https://openalex.org/W3102476541","https://openalex.org/W4285719527","https://openalex.org/W6628271527","https://openalex.org/W6646904105","https://openalex.org/W6678509853","https://openalex.org/W6989699103"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2101960027","https://openalex.org/W2197846993","https://openalex.org/W49697837","https://openalex.org/W2586575957","https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2913302899","https://openalex.org/W3204901196"],"abstract_inverted_index":{"In":[0,36],"this":[1,65],"paper,":[2],"we":[3],"present":[4],"a":[5,53,69],"machine":[6,81],"learning":[7,82],"classifier":[8,62],"which":[9],"is":[10,23],"used":[11,48],"for":[12],"pedestrian":[13,73,77,89,115,123],"detection":[14,74,116,124],"based":[15],"on":[16],"XGBoost.":[17],"Our":[18],"approach,":[19],"the":[20,27,40,87,95,120,126,132],"Genetic":[21],"Algorithm":[22],"introduced":[24],"to":[25,38,49,67],"optimize":[26],"parameter":[28],"tuning":[29],"process":[30],"during":[31],"training":[32,99],"an":[33],"XGBoost":[34],"model.":[35],"order":[37],"improve":[39],"classification":[41],"accuracy,":[42],"HOG":[43],"and":[44,80,105],"LBP":[45],"features":[46],"are":[47,83],"describe":[50],"pedestrians":[51],"in":[52,64,94,125],"way":[54],"of":[55,122],"tandem":[56],"fusion,":[57],"then":[58],"input":[59],"into":[60],"GA-XGBoost":[61],"proposed":[63],"paper":[66],"form":[68],"new":[70],"static":[71,127],"image":[72],"algorithm.":[75],"The":[76,129],"feature":[78,90,92],"extraction":[79],"decoupled":[84],"by":[85],"storing":[86],"extracted":[88],"as":[91],"files":[93],"experiment,":[96],"so":[97],"that":[98,113],"can":[100,107],"be":[101,108],"exacuted":[102],"many":[103],"times":[104],"algorithms":[106],"camparied":[109],"conveniently.":[110],"Experimental":[111],"show":[112],"our":[114],"algorithm":[117],"has":[118],"improved":[119],"accuracy":[121],"image.":[128],"Area":[130],"Under":[131],"ROC":[133],"Curve":[134],"(AUC)":[135],"value":[136],"reaches":[137],"0.9913.":[138]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":28},{"year":2024,"cited_by_count":30},{"year":2023,"cited_by_count":51},{"year":2022,"cited_by_count":53},{"year":2021,"cited_by_count":21},{"year":2020,"cited_by_count":17}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}