{"id":"https://openalex.org/W2901783607","doi":"https://doi.org/10.1109/tip.2018.2882926","title":"Moving Object Detection in Video via Hierarchical Modeling and Alternating Optimization","display_name":"Moving Object Detection in Video via Hierarchical Modeling and Alternating Optimization","publication_year":2018,"publication_date":"2018-11-22","ids":{"openalex":"https://openalex.org/W2901783607","doi":"https://doi.org/10.1109/tip.2018.2882926","mag":"2901783607","pmid":"https://pubmed.ncbi.nlm.nih.gov/30475720"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2018.2882926","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2882926","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5101677107","display_name":"Linhao Li","orcid":"https://orcid.org/0000-0001-7961-5320"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Linhao Li","raw_affiliation_strings":["Tianjin Key Lab of Machine Learning, School of Computer Science and Technology, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin Key Lab of Machine Learning, School of Computer Science and Technology, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056686459","display_name":"Qinghua Hu","orcid":"https://orcid.org/0000-0001-7765-8095"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghua Hu","raw_affiliation_strings":["Tianjin Key Lab of Machine Learning, School of Computer Science and Technology, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin Key Lab of Machine Learning, School of Computer Science and Technology, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100354039","display_name":"Xin Li","orcid":"https://orcid.org/0000-0003-2067-2763"},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Li","raw_affiliation_strings":["Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA"],"affiliations":[{"raw_affiliation_string":"Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA","institution_ids":["https://openalex.org/I12097938"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101677107"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":2.0892,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.90943127,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"28","issue":"4","first_page":"2021","last_page":"2036"},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9994000196456909,"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":0.9994000196456909,"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":0.8730641603469849},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7607282400131226},{"id":"https://openalex.org/keywords/foreground-detection","display_name":"Foreground detection","score":0.7028888463973999},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6322985291481018},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.6310427188873291},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6284788846969604},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6090178489685059},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5966236591339111},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.482749879360199},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.46082934737205505},{"id":"https://openalex.org/keywords/background-noise","display_name":"Background noise","score":0.4565564692020416},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4189383387565613},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4043002724647522},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.28316736221313477},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2704632878303528},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.19715869426727295},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1939619481563568}],"concepts":[{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.8730641603469849},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7607282400131226},{"id":"https://openalex.org/C2779769447","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Foreground detection","level":4,"score":0.7028888463973999},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6322985291481018},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.6310427188873291},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6284788846969604},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6090178489685059},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5966236591339111},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.482749879360199},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.46082934737205505},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.4565564692020416},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4189383387565613},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4043002724647522},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.28316736221313477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2704632878303528},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.19715869426727295},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1939619481563568},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2018.2882926","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2882926","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},{"id":"pmid:30475720","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/30475720","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7599999904632568}],"awards":[{"id":"https://openalex.org/G110109937","display_name":null,"funder_award_id":"61502332","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3332554125","display_name":null,"funder_award_id":"U1435212","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5783169264","display_name":null,"funder_award_id":"61432011","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6813436119","display_name":null,"funder_award_id":"61732011","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1499877760","https://openalex.org/W1526513203","https://openalex.org/W1532020452","https://openalex.org/W1953191904","https://openalex.org/W1960877884","https://openalex.org/W1969977005","https://openalex.org/W1977709942","https://openalex.org/W1990864775","https://openalex.org/W1993531312","https://openalex.org/W2002406878","https://openalex.org/W2018042101","https://openalex.org/W2021836772","https://openalex.org/W2024165284","https://openalex.org/W2033668463","https://openalex.org/W2035866593","https://openalex.org/W2052524720","https://openalex.org/W2059639989","https://openalex.org/W2060628274","https://openalex.org/W2066666057","https://openalex.org/W2071680411","https://openalex.org/W2082834618","https://openalex.org/W2087018183","https://openalex.org/W2091741383","https://openalex.org/W2095581126","https://openalex.org/W2096642693","https://openalex.org/W2097512404","https://openalex.org/W2098305432","https://openalex.org/W2100784318","https://openalex.org/W2102625004","https://openalex.org/W2112157293","https://openalex.org/W2115213191","https://openalex.org/W2121274305","https://openalex.org/W2127070222","https://openalex.org/W2131786818","https://openalex.org/W2140235142","https://openalex.org/W2142290030","https://openalex.org/W2143068965","https://openalex.org/W2143516773","https://openalex.org/W2145962650","https://openalex.org/W2150134853","https://openalex.org/W2150489380","https://openalex.org/W2164278908","https://openalex.org/W2168269905","https://openalex.org/W2182524050","https://openalex.org/W2233138502","https://openalex.org/W2282078292","https://openalex.org/W2394875178","https://openalex.org/W2511819774","https://openalex.org/W2512785428","https://openalex.org/W2538773003","https://openalex.org/W2551055131","https://openalex.org/W2608661295","https://openalex.org/W2618422320","https://openalex.org/W2713774261","https://openalex.org/W2750950678","https://openalex.org/W2768795283","https://openalex.org/W4292363360","https://openalex.org/W6629695367","https://openalex.org/W6631784836","https://openalex.org/W6674745927","https://openalex.org/W6674909129","https://openalex.org/W6684966483","https://openalex.org/W6728784960"],"related_works":["https://openalex.org/W2105103921","https://openalex.org/W2124189704","https://openalex.org/W1999137714","https://openalex.org/W2953123162","https://openalex.org/W2086414697","https://openalex.org/W2188430267","https://openalex.org/W4298195641","https://openalex.org/W2110416663","https://openalex.org/W3010714307","https://openalex.org/W2360289136"],"abstract_inverted_index":{"In":[0],"conventional":[1],"wisdom":[2],"of":[3,21,35,67,100,105,138,198],"video":[4,106,203],"modeling,":[5],"background":[6,22,31,68,83,170],"is":[7,16,72],"often":[8,193],"treated":[9],"as":[10,44,124,155],"the":[11,19,26,36,80,98,103,125],"primary":[12],"target":[13],"and":[14,30,48,57,69,84,94,171,192,206],"foreground":[15,29,70],"derived":[17],"using":[18],"technique":[20],"subtraction.":[23],"Based":[24],"on":[25,135],"observation":[27],"that":[28,164],"are":[32],"two":[33],"sides":[34],"same":[37],"coin,":[38],"we":[39,88,112,141],"propose":[40],"to":[41,73,127,144,175],"treat":[42],"them":[43],"peer":[45],"unknown":[46],"variables":[47],"formulate":[49],"a":[50,76,114,120,151],"joint":[51,148],"estimation":[52],"problem,":[53],"called":[54],"Hierarchical":[55],"modeling":[56],"Alternating":[58],"Optimization":[59],"(HMAO).":[60],"The":[61],"motivation":[62],"behind":[63],"our":[64,165],"hierarchical":[65,136],"extensions":[66,137],"models":[71],"better":[74,101,173],"incorporate":[75],"priori":[77],"knowledge":[78],"about":[79],"disparity":[81],"between":[82],"foreground.":[85],"For":[86],"background,":[87],"decompose":[89],"it":[90],"into":[91],"temporally":[92],"low-frequency":[93],"high-frequency":[95],"components":[96],"for":[97,110,202],"purpose":[99],"characterizing":[102],"class":[104],"with":[107],"dynamic":[108,205],"background;":[109],"foreground,":[111],"construct":[113],"Markov":[115],"random":[116],"field":[117],"prior":[118],"at":[119,131,189],"spatially":[121],"low":[122],"resolution":[123],"pivot":[126],"facilitate":[128],"noise-resilient":[129],"refinement":[130],"higher":[132],"resolutions.":[133],"Built":[134],"both":[139],"models,":[140],"show":[142],"how":[143],"successively":[145],"refine":[146],"their":[147],"estimates":[149],"under":[150],"unified":[152],"framework":[153],"known":[154],"alternating":[156],"direction":[157],"multipliers":[158],"method.":[159],"Experimental":[160],"results":[161],"have":[162],"shown":[163],"approach":[166],"produces":[167],"more":[168],"discriminative":[169],"demonstrates":[172],"robustness":[174],"noise":[176],"than":[177],"other":[178],"competing":[179],"methods.":[180],"When":[181],"compared":[182],"against":[183],"current":[184],"state-of-the-art":[185],"techniques,":[186],"HMAO":[187],"achieves":[188],"least":[190],"comparable":[191],"superior":[194],"performance":[195],"in":[196],"terms":[197],"F-measure":[199],"scores":[200],"especially":[201],"containing":[204],"complex":[207],"background.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}