{"id":"https://openalex.org/W2480730541","doi":"https://doi.org/10.3233/jifs-151674","title":"Vehicle detection in urban traffic scenes using the Pixel-Based Adaptive Segmenter with Confidence Measurement","display_name":"Vehicle detection in urban traffic scenes using the Pixel-Based Adaptive Segmenter with Confidence Measurement","publication_year":2016,"publication_date":"2016-07-22","ids":{"openalex":"https://openalex.org/W2480730541","doi":"https://doi.org/10.3233/jifs-151674","mag":"2480730541"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-151674","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-151674","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-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/A5076301736","display_name":"Yunsheng Zhang","orcid":"https://orcid.org/0000-0001-5630-4570"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunsheng Zhang","raw_affiliation_strings":["College of Transportation, Southeast University, Nanjing, PR China"],"affiliations":[{"raw_affiliation_string":"College of Transportation, Southeast University, Nanjing, PR China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087300333","display_name":"Chihang Zhao","orcid":"https://orcid.org/0000-0003-0315-4796"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chihang Zhao","raw_affiliation_strings":["College of Transportation, Southeast University, Nanjing, PR China"],"affiliations":[{"raw_affiliation_string":"College of Transportation, Southeast University, Nanjing, PR China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030291930","display_name":"Aiwei Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aiwei Chen","raw_affiliation_strings":["College of Transportation, Southeast University, Nanjing, PR China"],"affiliations":[{"raw_affiliation_string":"College of Transportation, Southeast University, Nanjing, PR China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068620920","display_name":"Xingzhi Qi","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingzhi Qi","raw_affiliation_strings":["College of Transportation, Southeast University, Nanjing, PR China"],"affiliations":[{"raw_affiliation_string":"College of Transportation, Southeast University, Nanjing, PR China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5087300333"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.334,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65445483,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"31","issue":"3","first_page":"1609","last_page":"1620"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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":1.0,"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":0.9990000128746033,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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/background-subtraction","display_name":"Background subtraction","score":0.8647507429122925},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7567643523216248},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6619794368743896},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5467220544815063},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5224634408950806},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.43297091126441956},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24853524565696716},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19858652353286743}],"concepts":[{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.8647507429122925},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7567643523216248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6619794368743896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5467220544815063},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5224634408950806},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.43297091126441956},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24853524565696716},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19858652353286743}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-151674","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-151674","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8500000238418579,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1963678626","https://openalex.org/W1969977005","https://openalex.org/W1971715095","https://openalex.org/W1977377723","https://openalex.org/W1986342516","https://openalex.org/W1988061476","https://openalex.org/W1990864775","https://openalex.org/W2000668593","https://openalex.org/W2002002766","https://openalex.org/W2026820593","https://openalex.org/W2030449681","https://openalex.org/W2033668463","https://openalex.org/W2035866593","https://openalex.org/W2048419187","https://openalex.org/W2061500429","https://openalex.org/W2071860582","https://openalex.org/W2072418182","https://openalex.org/W2082009928","https://openalex.org/W2083709814","https://openalex.org/W2101705628","https://openalex.org/W2102625004","https://openalex.org/W2115415549","https://openalex.org/W2116076678","https://openalex.org/W2127070222","https://openalex.org/W2128995878","https://openalex.org/W2130099717","https://openalex.org/W2131645848","https://openalex.org/W2131786818","https://openalex.org/W2134576786","https://openalex.org/W2156530496","https://openalex.org/W2158604775","https://openalex.org/W4300179783"],"related_works":["https://openalex.org/W2034462085","https://openalex.org/W2337415362","https://openalex.org/W2538483272","https://openalex.org/W2740820121","https://openalex.org/W2141888456","https://openalex.org/W2183878799","https://openalex.org/W3096301266","https://openalex.org/W2066730339","https://openalex.org/W2055234710","https://openalex.org/W1981023434"],"abstract_inverted_index":{"The":[0,37,68],"Pixel-Based":[1],"Adaptive":[2],"Segmenter":[3],"with":[4,124],"Confidence":[5],"Measurement":[6],"(PBASCM)":[7],"is":[8,28,39,57,80,94,172],"proposed":[9],"for":[10,143,176],"vehicle":[11,178],"detection":[12,108,179],"in":[13,53,95,117,157,180],"complex":[14,181],"urban":[15,101,182],"traffic":[16,66,86,102,159,183],"scenes":[17],"to":[18],"efficiently":[19],"address":[20],"deficiencies":[21],"of":[22,45,107],"the":[23,43,54,64,77,84,90,96,104,138,163],"background":[24,38,55,78],"subtraction":[25],"model,":[26],"which":[27],"easily":[29],"contaminated":[30],"by":[31,154],"slow-moving":[32,132],"or":[33,133],"temporarily":[34,134],"stopped":[35,135],"vehicles.":[36],"modeled":[40],"based":[41,62,82],"on":[42,63,72,83],"history":[44],"recently":[46],"observed":[47],"pixel":[48,52,92],"values":[49],"and":[50,88,120,137,140,147,174],"each":[51],"model":[56,79],"assigned":[58],"a":[59,158],"confidence":[60,97],"measurement":[61],"current":[65,85],"state.":[67],"foreground":[69],"decision":[70],"depends":[71],"an":[73],"adaptive":[74],"threshold,":[75],"whereas":[76],"updated":[81],"state":[87],"whether":[89],"corresponding":[91],"point":[93],"period.":[98],"Using":[99],"real-world":[100],"videos,":[103],"overall":[105],"results":[106,142,168],"accuracy":[109],"analyses":[110],"demonstrated":[111],"that":[112,170],"PBASCM":[113,128,144,171],"achieved":[114],"better":[115],"performance":[116],"both":[118],"qualitative":[119],"quantitative":[121],"evaluations,":[122],"compared":[123],"other":[125,155],"state-of-the-art":[126],"methods.":[127],"can":[129],"accurately":[130],"detect":[131],"vehicles,":[136],"similarity":[139],"F-measure":[141],"were":[145],"0.839":[146],"0.912":[148],"higher,":[149],"respectively,":[150],"than":[151],"those":[152],"obtained":[153],"methods":[156],"light":[160],"sequence":[161],"during":[162],"daytime.":[164],"Thus,":[165],"our":[166],"experimental":[167],"demonstrate":[169],"effective":[173],"suitable":[175],"real-time":[177],"scenes.":[184]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
