{"id":"https://openalex.org/W2897328611","doi":"https://doi.org/10.1109/fuzz-ieee.2018.8491497","title":"Evolving Background Subtraction for Dynamic Lighting Scenarios","display_name":"Evolving Background Subtraction for Dynamic Lighting Scenarios","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2897328611","doi":"https://doi.org/10.1109/fuzz-ieee.2018.8491497","mag":"2897328611"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz-ieee.2018.8491497","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz-ieee.2018.8491497","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 Fuzzy Systems (FUZZ-IEEE)","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/A5043160730","display_name":"Bruno Sielly Jales Costa","orcid":"https://orcid.org/0000-0002-3039-4632"},"institutions":[{"id":"https://openalex.org/I1292974536","display_name":"Ford Motor Company (United States)","ror":"https://ror.org/00g2tkw06","country_code":"US","type":"company","lineage":["https://openalex.org/I1292974536"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Bruno Sielly Jales Costa","raw_affiliation_strings":["Research & Innovation Center Ford Motor Company, Palo Alto, USA"],"affiliations":[{"raw_affiliation_string":"Research & Innovation Center Ford Motor Company, Palo Alto, USA","institution_ids":["https://openalex.org/I1292974536"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010171771","display_name":"Madeline J. Goh","orcid":null},"institutions":[{"id":"https://openalex.org/I1292974536","display_name":"Ford Motor Company (United States)","ror":"https://ror.org/00g2tkw06","country_code":"US","type":"company","lineage":["https://openalex.org/I1292974536"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Madeline Goh","raw_affiliation_strings":["Research & Innovation Center Ford Motor Company, Palo Alto, USA"],"affiliations":[{"raw_affiliation_string":"Research & Innovation Center Ford Motor Company, Palo Alto, USA","institution_ids":["https://openalex.org/I1292974536"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5043160730"],"corresponding_institution_ids":["https://openalex.org/I1292974536"],"apc_list":null,"apc_paid":null,"fwci":0.1045,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45887058,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998000264167786,"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":0.9998000264167786,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9977999925613403,"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/T11963","display_name":"Impact of Light on Environment and Health","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.9147266149520874},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6892132759094238},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6583649516105652},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6487410664558411},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6221067309379578},{"id":"https://openalex.org/keywords/normality","display_name":"Normality","score":0.5298062562942505},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5287964940071106},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49595484137535095},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4676513075828552},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3200809359550476},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19799301028251648},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09121531248092651}],"concepts":[{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.9147266149520874},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6892132759094238},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6583649516105652},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6487410664558411},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6221067309379578},{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.5298062562942505},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5287964940071106},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49595484137535095},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4676513075828552},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3200809359550476},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19799301028251648},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09121531248092651},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz-ieee.2018.8491497","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz-ieee.2018.8491497","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 Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5600000023841858,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1573546770","https://openalex.org/W1654125355","https://openalex.org/W2035866593","https://openalex.org/W2071860582","https://openalex.org/W2083978531","https://openalex.org/W2093249965","https://openalex.org/W2095445014","https://openalex.org/W2102625004","https://openalex.org/W2115213191","https://openalex.org/W2124592697","https://openalex.org/W2130306094","https://openalex.org/W2130416896","https://openalex.org/W2134004675","https://openalex.org/W2141572457","https://openalex.org/W2151928481","https://openalex.org/W2168456609","https://openalex.org/W2405038777","https://openalex.org/W2464430814","https://openalex.org/W2465549242","https://openalex.org/W2528980099","https://openalex.org/W2540438180","https://openalex.org/W2555210016","https://openalex.org/W2963212638","https://openalex.org/W6634202927","https://openalex.org/W6679349572","https://openalex.org/W6728201021","https://openalex.org/W6746472748","https://openalex.org/W6884942452"],"related_works":["https://openalex.org/W4321650139","https://openalex.org/W2169275958","https://openalex.org/W2106922074","https://openalex.org/W2917687159","https://openalex.org/W2000721663","https://openalex.org/W2506314341","https://openalex.org/W2405714784","https://openalex.org/W4391002904","https://openalex.org/W2188430267","https://openalex.org/W2427283480"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,74,132,137],"novel":[4],"approach":[5,25],"to":[6,35,66,79],"background":[7,82,96,128],"subtraction":[8,129],"based":[9,99,115],"on":[10,100,116,131],"the":[11,81,84,109,112],"concepts":[12],"of":[13,31,39,108,111,134,136],"typicality":[14],"and":[15,19],"eccentricity":[16,103],"data":[17,48],"analytics":[18],"self-evolving":[20],"cloud-based":[21],"classification.":[22],"The":[23,120,141],"proposed":[24,121],"creates":[26],"local":[27],"(i.e.":[28],"specific":[29],"region":[30,91],"an":[32,105],"image,":[33],"up":[34],"pixel":[36,88],"level)":[37],"models":[38],"normality":[40,52,75],"that":[41],"can":[42,54],"be":[43],"recursively":[44],"updated":[45],"as":[46,95],"new":[47],"are":[49,144],"acquired.":[50],"Each":[51,87],"model":[53,76],"represent":[55],"different":[56,61],"normal":[57],"scenarios,":[58],"often":[59],"significantly":[60],"from":[62],"each":[63],"other":[64],"due":[65],"dynamic":[67],"lighting":[68,150],"(e.g.":[69],"moving":[70,138],"shadows,":[71],"sunspots).":[72],"Such":[73],"is":[77,92,123],"used":[78],"describe":[80],"for":[83],"image":[85,90,118],"stream.":[86],"or":[89,97],"then":[93],"classificated":[94],"foreground":[98],"its":[101],"calculated":[102],"level,":[104],"aggregated":[106],"measurement":[107],"intensities":[110],"RGB":[113],"channels":[114],"previous":[117],"samples.":[119],"technique":[122],"compared":[124],"with":[125],"nine":[126],"well-known":[127],"algorithms":[130],"set":[133],"images":[135],"vehicle":[139],"interior.":[140],"results":[142],"obtained":[143],"very":[145],"promising,":[146],"especially":[147],"under":[148],"challenging":[149],"scenarios.":[151]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
