{"id":"https://openalex.org/W3039529896","doi":"https://doi.org/10.1109/tits.2020.3001687","title":"Tensor-Based Approach for Background-Foreground Separation in Maritime Sequences","display_name":"Tensor-Based Approach for Background-Foreground Separation in Maritime Sequences","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3039529896","doi":"https://doi.org/10.1109/tits.2020.3001687","mag":"3039529896"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2020.3001687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2020.3001687","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation 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/A5075531078","display_name":"Ibrahim Kajo","orcid":"https://orcid.org/0000-0002-0102-0279"},"institutions":[{"id":"https://openalex.org/I37553959","display_name":"Universit\u00e9 de technologie de belfort-montb\u00e9liard","ror":"https://ror.org/05bn3m307","country_code":"FR","type":"education","lineage":["https://openalex.org/I37553959"]},{"id":"https://openalex.org/I4210118524","display_name":"Universit\u00e9 Bourgogne Franche-Comt\u00e9","ror":"https://ror.org/02dn7x778","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210118524"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Ibrahim Kajo","raw_affiliation_strings":["Laboratoire CIAD UMR 7533, Universit\u00e9 Bourgogne Franche-Comt\u00e9, UTBM, Belfort, France"],"raw_orcid":"https://orcid.org/0000-0002-0102-0279","affiliations":[{"raw_affiliation_string":"Laboratoire CIAD UMR 7533, Universit\u00e9 Bourgogne Franche-Comt\u00e9, UTBM, Belfort, France","institution_ids":["https://openalex.org/I37553959","https://openalex.org/I4210118524"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071162645","display_name":"Nidal Kamel","orcid":"https://orcid.org/0000-0002-9638-6379"},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Nidal Kamel","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Centre for Intelligent Signal & Imaging Research, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia"],"raw_orcid":"https://orcid.org/0000-0002-9638-6379","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Centre for Intelligent Signal & Imaging Research, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia","institution_ids":["https://openalex.org/I203899302"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083239938","display_name":"Yassine Ruichek","orcid":"https://orcid.org/0000-0003-4795-8569"},"institutions":[{"id":"https://openalex.org/I37553959","display_name":"Universit\u00e9 de technologie de belfort-montb\u00e9liard","ror":"https://ror.org/05bn3m307","country_code":"FR","type":"education","lineage":["https://openalex.org/I37553959"]},{"id":"https://openalex.org/I4210118524","display_name":"Universit\u00e9 Bourgogne Franche-Comt\u00e9","ror":"https://ror.org/02dn7x778","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210118524"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Yassine Ruichek","raw_affiliation_strings":["Laboratoire CIAD UMR 7533, Universit\u00e9 Bourgogne Franche-Comt\u00e9, UTBM, Belfort, France"],"raw_orcid":"https://orcid.org/0000-0003-4795-8569","affiliations":[{"raw_affiliation_string":"Laboratoire CIAD UMR 7533, Universit\u00e9 Bourgogne Franche-Comt\u00e9, UTBM, Belfort, France","institution_ids":["https://openalex.org/I37553959","https://openalex.org/I4210118524"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5075531078"],"corresponding_institution_ids":["https://openalex.org/I37553959","https://openalex.org/I4210118524"],"apc_list":null,"apc_paid":null,"fwci":0.4906,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.65771934,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"22","issue":"11","first_page":"7115","last_page":"7128"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9995999932289124,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9995999932289124,"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":0.9990000128746033,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9975000023841858,"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":0.7207586765289307},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6258122324943542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5658348798751831},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49401718378067017},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.477211058139801},{"id":"https://openalex.org/keywords/separation","display_name":"Separation (statistics)","score":0.43873730301856995},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4305001497268677},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.4228241443634033},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40469837188720703},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35599637031555176},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23353919386863708},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.2135084569454193},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19456696510314941}],"concepts":[{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.7207586765289307},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6258122324943542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5658348798751831},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49401718378067017},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.477211058139801},{"id":"https://openalex.org/C2776061190","wikidata":"https://www.wikidata.org/wiki/Q7451805","display_name":"Separation (statistics)","level":2,"score":0.43873730301856995},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4305001497268677},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.4228241443634033},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40469837188720703},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35599637031555176},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23353919386863708},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2135084569454193},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19456696510314941},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"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/tits.2020.3001687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2020.3001687","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life below water","score":0.8299999833106995,"id":"https://metadata.un.org/sdg/14"}],"awards":[{"id":"https://openalex.org/G6557732655","display_name":null,"funder_award_id":"YUTP:0153AA-H41","funder_id":"https://openalex.org/F4320323380","funder_display_name":"Universiti Teknologi Petronas"}],"funders":[{"id":"https://openalex.org/F4320323380","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W50428016","https://openalex.org/W1481797078","https://openalex.org/W1499877760","https://openalex.org/W1551409130","https://openalex.org/W1736339626","https://openalex.org/W1919155910","https://openalex.org/W1999136078","https://openalex.org/W2005853603","https://openalex.org/W2021836772","https://openalex.org/W2024165284","https://openalex.org/W2025603201","https://openalex.org/W2040441737","https://openalex.org/W2058078260","https://openalex.org/W2059639989","https://openalex.org/W2060204507","https://openalex.org/W2065914597","https://openalex.org/W2071860582","https://openalex.org/W2078459001","https://openalex.org/W2083978531","https://openalex.org/W2084885787","https://openalex.org/W2088668249","https://openalex.org/W2098305432","https://openalex.org/W2102227437","https://openalex.org/W2102625004","https://openalex.org/W2110764733","https://openalex.org/W2115213191","https://openalex.org/W2120865781","https://openalex.org/W2130293653","https://openalex.org/W2132538571","https://openalex.org/W2133140216","https://openalex.org/W2136494725","https://openalex.org/W2139047213","https://openalex.org/W2140235142","https://openalex.org/W2141572457","https://openalex.org/W2142077116","https://openalex.org/W2145962650","https://openalex.org/W2147190441","https://openalex.org/W2153689886","https://openalex.org/W2158403369","https://openalex.org/W2164278908","https://openalex.org/W2164720308","https://openalex.org/W2241814552","https://openalex.org/W2263768404","https://openalex.org/W2476422535","https://openalex.org/W2501988756","https://openalex.org/W2511125977","https://openalex.org/W2551589584","https://openalex.org/W2552331162","https://openalex.org/W2566016175","https://openalex.org/W2570149284","https://openalex.org/W2626779203","https://openalex.org/W2793896956","https://openalex.org/W2805669012","https://openalex.org/W2811030020","https://openalex.org/W2911993024","https://openalex.org/W2952100474","https://openalex.org/W2961605659","https://openalex.org/W3111652977","https://openalex.org/W4248936881","https://openalex.org/W6628749493","https://openalex.org/W6632919905","https://openalex.org/W6640220554","https://openalex.org/W6677937869","https://openalex.org/W6681016373","https://openalex.org/W6681629389","https://openalex.org/W6724497479","https://openalex.org/W6786990498","https://openalex.org/W6929385289"],"related_works":["https://openalex.org/W2569661359","https://openalex.org/W2781510240","https://openalex.org/W2950186459","https://openalex.org/W2170114491","https://openalex.org/W4292636185","https://openalex.org/W2766870359","https://openalex.org/W2897298721","https://openalex.org/W2242624680","https://openalex.org/W2136127937","https://openalex.org/W1551449546"],"abstract_inverted_index":{"The":[0,78,91,172,193],"complexity":[1],"of":[2,57,61,75,113,144,174,200,209],"a":[3,51,62,81,103,142,148],"scene":[4],"in":[5,46,153,217],"addition":[6],"to":[7,41,96,102,132,157,213],"the":[8,14,48,55,73,120,127,134,137,154,175,201,207,210,215],"need":[9],"for":[10,23,88],"real-time":[11],"processing":[12],"are":[13,191],"main":[15],"challenges":[16,100,160,203],"that":[17],"face":[18],"any":[19],"background/foreground":[20,89],"separation":[21,63,128,216],"approach":[22,87,92,122,156],"maritime":[24,104,189],"environment.":[25],"Recent":[26],"studies":[27],"on":[28,136,185],"<italic":[29,163],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[30,164],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Low-rank":[31],"and":[32,116,169,182,204],"Sparse":[33],"Separation</i>":[34],"(LSS)":[35],"achieved":[36],"good":[37],"performance":[38,173,197],"when":[39,141],"compared":[40],"traditional":[42],"background":[43],"subtraction":[44],"techniques":[45,184],"segregating":[47],"foreground":[49,114],"from":[50],"complex":[52,188],"background.":[53],"However,":[54],"issue":[56],"maintaining":[58],"this":[59],"type":[60],"via":[64,125],"an":[65],"updating":[66,126],"mechanism":[67,150],"is":[68,93,151],"not":[69],"well":[70],"addressed":[71],"by":[72],"majority":[74],"LSS":[76,181],"approaches.":[77],"study":[79],"presents":[80],"tensor":[82],"based":[83],"singular":[84],"value":[85],"decomposition":[86,135],"separation.":[90],"uniquely":[94],"designed":[95],"deal":[97],"with":[98,178,187],"most":[99,199],"related":[101],"environment":[105],"such":[106,161],"as":[107,130,162],"sea":[108],"dynamics,":[109],"boat":[110],"wakes,":[111],"variety":[112],"objects,":[115],"camera":[117],"jitter.":[118],"Furthermore,":[119],"proposed":[121,155,176,211],"operates":[123],"incrementally":[124],"components":[129],"opposed":[131],"reperforming":[133],"entire":[138],"video":[139],"sequence":[140],"set":[143],"frames":[145],"arrives.":[146],"Additionally,":[147],"forgetting":[149],"employed":[152],"efficiently":[158],"handle":[159],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Stationary":[165],"Foreground":[166],"Objects</i>":[167],"(SFOs)":[168],"ghost":[170],"effects.":[171],"method":[177,212],"several":[179],"state-of-the-art":[180],"Non-LSS":[183],"videos":[186],"scenarios":[190],"evaluated.":[192],"results":[194],"exhibit":[195],"better":[196],"over":[198],"tested":[202],"also":[205],"demonstrate":[206],"capability":[208],"perform":[214],"less":[218],"computational":[219],"time.":[220]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
