{"id":"https://openalex.org/W4308080091","doi":"https://doi.org/10.1109/itsc55140.2022.9922411","title":"Selecting Adequate Basis Model for Adaptation in Pedestrian Detection","display_name":"Selecting Adequate Basis Model for Adaptation in Pedestrian Detection","publication_year":2022,"publication_date":"2022-10-08","ids":{"openalex":"https://openalex.org/W4308080091","doi":"https://doi.org/10.1109/itsc55140.2022.9922411"},"language":"en","primary_location":{"id":"doi:10.1109/itsc55140.2022.9922411","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc55140.2022.9922411","pdf_url":null,"source":{"id":"https://openalex.org/S4363607737","display_name":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","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/A5087686172","display_name":"Tu\u011f\u00e7e Toprak","orcid":"https://orcid.org/0000-0003-2176-5822"},"institutions":[{"id":"https://openalex.org/I193615760","display_name":"Dokuz Eyl\u00fcl University","ror":"https://ror.org/00dbd8b73","country_code":"TR","type":"education","lineage":["https://openalex.org/I193615760"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Tugce Toprak","raw_affiliation_strings":["Dokuz Eylul University,Faculty of Electrical and Electronics Engineering,Izmir,Turkey","Faculty of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey"],"affiliations":[{"raw_affiliation_string":"Dokuz Eylul University,Faculty of Electrical and Electronics Engineering,Izmir,Turkey","institution_ids":["https://openalex.org/I193615760"]},{"raw_affiliation_string":"Faculty of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey","institution_ids":["https://openalex.org/I193615760"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062985885","display_name":"Mustafa Ozcelikors","orcid":null},"institutions":[{"id":"https://openalex.org/I120217413","display_name":"Izmir Institute of Technology","ror":"https://ror.org/03stptj97","country_code":"TR","type":"education","lineage":["https://openalex.org/I120217413"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Mustafa Ozcelikors","raw_affiliation_strings":["TTTech Auto Turkey,Izmir,Turkey","TTTech Auto Turkey, Izmir, Turkey"],"affiliations":[{"raw_affiliation_string":"TTTech Auto Turkey,Izmir,Turkey","institution_ids":["https://openalex.org/I120217413"]},{"raw_affiliation_string":"TTTech Auto Turkey, Izmir, Turkey","institution_ids":["https://openalex.org/I120217413"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071792338","display_name":"M. Alper Selver","orcid":"https://orcid.org/0000-0002-8445-0388"},"institutions":[{"id":"https://openalex.org/I193615760","display_name":"Dokuz Eyl\u00fcl University","ror":"https://ror.org/00dbd8b73","country_code":"TR","type":"education","lineage":["https://openalex.org/I193615760"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"M.Alper Selver","raw_affiliation_strings":["Dokuz Eylul University,Faculty of Electrical and Electronics Engineering,Izmir,Turkey","Faculty of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey"],"affiliations":[{"raw_affiliation_string":"Dokuz Eylul University,Faculty of Electrical and Electronics Engineering,Izmir,Turkey","institution_ids":["https://openalex.org/I193615760"]},{"raw_affiliation_string":"Faculty of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey","institution_ids":["https://openalex.org/I193615760"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5087686172"],"corresponding_institution_ids":["https://openalex.org/I193615760"],"apc_list":null,"apc_paid":null,"fwci":0.1199,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.4201483,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1652","last_page":"1655"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9994999766349792,"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":0.9994999766349792,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9970999956130981,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.996399998664856,"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/computer-science","display_name":"Computer science","score":0.7950174808502197},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6276752948760986},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6031659841537476},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.558945894241333},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5171334147453308},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5153874158859253},{"id":"https://openalex.org/keywords/usability","display_name":"Usability","score":0.45276299118995667},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.41934049129486084},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3271225094795227},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.2268841564655304}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7950174808502197},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6276752948760986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6031659841537476},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.558945894241333},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5171334147453308},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5153874158859253},{"id":"https://openalex.org/C170130773","wikidata":"https://www.wikidata.org/wiki/Q216378","display_name":"Usability","level":2,"score":0.45276299118995667},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.41934049129486084},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3271225094795227},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.2268841564655304},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc55140.2022.9922411","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc55140.2022.9922411","pdf_url":null,"source":{"id":"https://openalex.org/S4363607737","display_name":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2031489346","https://openalex.org/W2107775979","https://openalex.org/W2610165754","https://openalex.org/W2805394695","https://openalex.org/W2962785096","https://openalex.org/W2982303846","https://openalex.org/W3016302360","https://openalex.org/W3105582502","https://openalex.org/W3110409447","https://openalex.org/W4287078747","https://openalex.org/W6639102338","https://openalex.org/W6839956189"],"related_works":["https://openalex.org/W4389670110","https://openalex.org/W2429057255","https://openalex.org/W2187546663","https://openalex.org/W148745890","https://openalex.org/W2611942503","https://openalex.org/W2378211422","https://openalex.org/W4315621326","https://openalex.org/W2899790217","https://openalex.org/W2598865957","https://openalex.org/W1576092969"],"abstract_inverted_index":{"The":[0,150],"emerging":[1],"machine":[2],"learning":[3,46],"methods":[4],"and":[5,9,35,81,87,109,121],"models":[6],"require":[7],"large":[8],"well-annotated":[10],"benchmark":[11,61],"datasets":[12],"in":[13],"order":[14],"to":[15,117,169],"increase":[16],"their":[17],"generalization":[18],"capacity.":[19],"Pedestrian":[20],"detection":[21],"is":[22,76,139],"one":[23],"of":[24,40,72,123,147,156,159],"the":[25,41,70,119,154,165],"major":[26],"research":[27],"fields":[28],"that":[29,153],"suffer":[30],"from":[31],"data":[32,126],"set":[33,127],"dependency":[34],"over-fitting.":[36],"A":[37],"significant":[38],"portion":[39],"ongoing":[42],"studies":[43],"uses":[44],"transfer":[45],"or":[47],"similar":[48],"adaptation":[49,172],"strategies":[50],"based":[51,78],"on":[52,59,79,128],"deep":[53],"models,":[54],"which":[55,83,114],"are":[56,115],"initially":[57],"trained":[58],"a":[60,85,96,103,124,136,144],"dataset.":[62,132],"This":[63,93],"paper":[64,94],"searches":[65],"for":[66,101],"an":[67,73,129],"answer.":[68],"Unfortunately,":[69],"selection":[71],"adequate":[74],"model":[75],"usually":[77],"trial":[80],"error,":[82],"requires":[84],"tedious":[86],"time-consuming":[88],"simulation":[89],"through":[90],"potential":[91],"candidates.":[92],"proposes":[95],"computationally":[97],"simple":[98],"analysis":[99,146],"strategy":[100],"selecting":[102],"good":[104],"candidate":[105],"using":[106],"Shannon":[107],"Entropy":[108],"Gray":[110],"Level":[111],"Co-occurrence":[112],"Matrices,":[113],"utilized":[116],"determine":[118],"similarity":[120,155],"usability":[122],"pre-trained":[125],"independent":[130],"test":[131],"For":[133],"this":[134],"purpose,":[135],"new":[137],"dataset":[138],"also":[140],"presented":[141],"together":[142],"with":[143],"detailed":[145],"its":[148],"characteristics.":[149],"results":[151],"show":[152],"entropy":[157],"distribution":[158],"annotations":[160],"(i.e.":[161],"bounding":[162],"boxes)":[163],"has":[164],"most":[166],"important":[167],"characteristic":[168],"achieve":[170],"robust":[171],"after":[173],"training,":[174]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}