{"id":"https://openalex.org/W1960877884","doi":"https://doi.org/10.1109/cvpr.2015.7299099","title":"Background Subtraction via generalized fused lasso foreground modeling","display_name":"Background Subtraction via generalized fused lasso foreground modeling","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1960877884","doi":"https://doi.org/10.1109/cvpr.2015.7299099","mag":"1960877884"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7299099","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7299099","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5053655489","display_name":"Bo Xin","orcid":"https://orcid.org/0000-0002-0863-2198"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bo Xin","raw_affiliation_strings":["Peking University, Beijing, Beijing, CN","Nat'l Engineering Laboratory for Video Technology, Cooperative Medianet Innovation Center, Key Laboratory of Machine Perception (MoE), Sch'l of EECS, Peking University, Beijing, 100871, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, Beijing, CN","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Nat'l Engineering Laboratory for Video Technology, Cooperative Medianet Innovation Center, Key Laboratory of Machine Perception (MoE), Sch'l of EECS, Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100716460","display_name":"Yuan Tian","orcid":"https://orcid.org/0000-0002-9097-4639"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Tian","raw_affiliation_strings":["Nat'l Engineering Laboratory for Video Technology, Peking University, Beijing, China","Nat'l Engineering Laboratory for Video Technology, Cooperative Medianet Innovation Center, Key Laboratory of Machine Perception (MoE), Sch'l of EECS, Peking University, Beijing, 100871, China"],"affiliations":[{"raw_affiliation_string":"Nat'l Engineering Laboratory for Video Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Nat'l Engineering Laboratory for Video Technology, Cooperative Medianet Innovation Center, Key Laboratory of Machine Perception (MoE), Sch'l of EECS, Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100602395","display_name":"Yizhou Wang","orcid":"https://orcid.org/0000-0001-9888-6409"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yizhou Wang","raw_affiliation_strings":["Nat'l Engineering Laboratory for Video Technology, Peking University, Beijing, China","Nat'l Engineering Laboratory for Video Technology, Cooperative Medianet Innovation Center, Key Laboratory of Machine Perception (MoE), Sch'l of EECS, Peking University, Beijing, 100871, China"],"affiliations":[{"raw_affiliation_string":"Nat'l Engineering Laboratory for Video Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Nat'l Engineering Laboratory for Video Technology, Cooperative Medianet Innovation Center, Key Laboratory of Machine Perception (MoE), Sch'l of EECS, Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018478553","display_name":"Wen Gao","orcid":"https://orcid.org/0000-0002-8070-802X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Gao","raw_affiliation_strings":["Peking University, Beijing, Beijing, CN","Nat'l Engineering Laboratory for Video Technology, Cooperative Medianet Innovation Center, Key Laboratory of Machine Perception (MoE), Sch'l of EECS, Peking University, Beijing, 100871, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, Beijing, CN","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Nat'l Engineering Laboratory for Video Technology, Cooperative Medianet Innovation Center, Key Laboratory of Machine Perception (MoE), Sch'l of EECS, Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5053655489"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":8.236,"has_fulltext":false,"cited_by_count":89,"citation_normalized_percentile":{"value":0.98356511,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"4676","last_page":"4684"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9997000098228455,"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":0.9997000098228455,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.9965999722480774,"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":0.8017233610153198},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6355149745941162},{"id":"https://openalex.org/keywords/foreground-detection","display_name":"Foreground detection","score":0.6116871237754822},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6038428544998169},{"id":"https://openalex.org/keywords/lagrange-multiplier","display_name":"Lagrange multiplier","score":0.5911798477172852},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5195420384407043},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4859229624271393},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.447787344455719},{"id":"https://openalex.org/keywords/augmented-lagrangian-method","display_name":"Augmented Lagrangian method","score":0.4277648329734802},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4116177558898926},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36100590229034424},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3534330129623413},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.30134379863739014},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.2943207621574402}],"concepts":[{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.8017233610153198},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6355149745941162},{"id":"https://openalex.org/C2779769447","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Foreground detection","level":4,"score":0.6116871237754822},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6038428544998169},{"id":"https://openalex.org/C73684929","wikidata":"https://www.wikidata.org/wiki/Q598870","display_name":"Lagrange multiplier","level":2,"score":0.5911798477172852},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5195420384407043},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4859229624271393},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.447787344455719},{"id":"https://openalex.org/C150452318","wikidata":"https://www.wikidata.org/wiki/Q4820432","display_name":"Augmented Lagrangian method","level":2,"score":0.4277648329734802},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4116177558898926},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36100590229034424},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3534330129623413},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.30134379863739014},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2943207621574402},{"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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"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":2,"locations":[{"id":"doi:10.1109/cvpr.2015.7299099","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7299099","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.981.7196","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.981.7196","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xin_Background_Subtraction_via_2015_CVPR_paper.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W33208438","https://openalex.org/W82327102","https://openalex.org/W183902185","https://openalex.org/W1572873151","https://openalex.org/W1669104078","https://openalex.org/W1736339626","https://openalex.org/W2011425943","https://openalex.org/W2013469283","https://openalex.org/W2035866593","https://openalex.org/W2056636001","https://openalex.org/W2069057506","https://openalex.org/W2072595688","https://openalex.org/W2097512404","https://openalex.org/W2100556411","https://openalex.org/W2101399927","https://openalex.org/W2102625004","https://openalex.org/W2103972604","https://openalex.org/W2115213191","https://openalex.org/W2119300483","https://openalex.org/W2127070222","https://openalex.org/W2135046866","https://openalex.org/W2141572457","https://openalex.org/W2145962650","https://openalex.org/W2148290050","https://openalex.org/W2150489380","https://openalex.org/W2169232245","https://openalex.org/W2256627038","https://openalex.org/W2953361807","https://openalex.org/W3098745759","https://openalex.org/W6634277797","https://openalex.org/W6692055640","https://openalex.org/W6929385289"],"related_works":["https://openalex.org/W2015060724","https://openalex.org/W2058578573","https://openalex.org/W2012871340","https://openalex.org/W3046535663","https://openalex.org/W4317581747","https://openalex.org/W3035320230","https://openalex.org/W4287694146","https://openalex.org/W3207854495","https://openalex.org/W2915353907","https://openalex.org/W2267953278"],"abstract_inverted_index":{"Background":[0],"Subtraction":[1],"(BS)":[2],"is":[3,147],"one":[4],"of":[5,43,165],"the":[6,37,70,74,78,102,109,112,132,151,163,166],"key":[7],"steps":[8],"in":[9,121,138],"video":[10],"analysis.":[11],"Many":[12],"background":[13,35,48,85,105,116],"models":[14],"have":[15],"been":[16],"proposed":[17,110,167],"and":[18,104],"achieved":[19],"promising":[20],"performance":[21],"on":[22,82,156],"public":[23],"data":[24,160],"sets.":[25],"However,":[26],"due":[27],"to":[28,59,170],"challenges":[29],"such":[30,72,139],"as":[31,45,47,73,92],"illumination":[32],"change,":[33],"dynamic":[34],"etc.":[36],"resulted":[38],"foreground":[39,103,152],"segmentation":[40],"often":[41],"consists":[42],"holes":[44],"well":[46],"noise.":[49],"In":[50],"this":[51],"regard,":[52],"we":[53,89],"consider":[54],"generalized":[55],"fused":[56],"lasso":[57],"regularization":[58,98],"quest":[60],"for":[61,100,149],"intact":[62],"structured":[63],"foregrounds.":[64],"Together":[65],"with":[66],"certain":[67],"assumptions":[68,117],"about":[69],"background,":[71],"low-rank":[75],"assumption":[76,80],"or":[77],"sparse-composition":[79],"(depending":[81],"whether":[83],"pure":[84],"frames":[86],"are":[87],"provided),":[88],"formulate":[90],"BS":[91,159],"a":[93,122,140,143],"matrix":[94],"decomposition":[95],"problem":[96],"using":[97],"terms":[99],"both":[101],"matrices.":[106],"Moreover,":[107],"under":[108],"formulation,":[111],"two":[113],"generally":[114],"distinctive":[115],"can":[118],"be":[119],"solved":[120],"unified":[123],"manner.":[124],"The":[125],"optimization":[126],"was":[127],"carried":[128],"out":[129],"via":[130],"applying":[131],"augmented":[133],"Lagrange":[134],"multiplier":[135],"(ALM)":[136],"method":[137],"way":[141],"that":[142],"fast":[144],"parametric-flow":[145],"algorithm":[146],"used":[148],"updating":[150],"matrix.":[153],"Experimental":[154],"results":[155],"several":[157],"popular":[158],"sets":[161],"demonstrate":[162],"advantage":[164],"model":[168],"compared":[169],"state-of-the-arts.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":16},{"year":2018,"cited_by_count":17},{"year":2017,"cited_by_count":12},{"year":2016,"cited_by_count":12},{"year":2015,"cited_by_count":3}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
