{"id":"https://openalex.org/W2903118147","doi":"https://doi.org/10.1109/tits.2018.2880096","title":"MSFgNet: A Novel Compact End-to-End Deep Network for Moving Object Detection","display_name":"MSFgNet: A Novel Compact End-to-End Deep Network for Moving Object Detection","publication_year":2018,"publication_date":"2018-11-27","ids":{"openalex":"https://openalex.org/W2903118147","doi":"https://doi.org/10.1109/tits.2018.2880096","mag":"2903118147"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2018.2880096","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2018.2880096","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/A5050348715","display_name":"Prashant W. Patil","orcid":"https://orcid.org/0000-0003-2604-6501"},"institutions":[{"id":"https://openalex.org/I119241673","display_name":"Indian Institute of Technology Ropar","ror":"https://ror.org/02qkhhn56","country_code":"IN","type":"education","lineage":["https://openalex.org/I119241673"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Prashant W. Patil","raw_affiliation_strings":["Department of Electrical Engineering, Computer Vision and Pattern Recognition Laboratory, IIT Ropar, Rupnagar, India"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Computer Vision and Pattern Recognition Laboratory, IIT Ropar, Rupnagar, India","institution_ids":["https://openalex.org/I119241673"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032409915","display_name":"Subrahmanyam Murala","orcid":"https://orcid.org/0000-0003-3384-4368"},"institutions":[{"id":"https://openalex.org/I119241673","display_name":"Indian Institute of Technology Ropar","ror":"https://ror.org/02qkhhn56","country_code":"IN","type":"education","lineage":["https://openalex.org/I119241673"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Subrahmanyam Murala","raw_affiliation_strings":["Department of Electrical Engineering, Computer Vision and Pattern Recognition Laboratory, IIT Ropar, Rupnagar, India"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Computer Vision and Pattern Recognition Laboratory, IIT Ropar, Rupnagar, India","institution_ids":["https://openalex.org/I119241673"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5050348715"],"corresponding_institution_ids":["https://openalex.org/I119241673"],"apc_list":null,"apc_paid":null,"fwci":5.1184,"has_fulltext":false,"cited_by_count":110,"citation_normalized_percentile":{"value":0.96752749,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"20","issue":"11","first_page":"4066","last_page":"4077"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9998000264167786,"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/T11605","display_name":"Visual Attention and Saliency Detection","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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7961558103561401},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7515701055526733},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6802269816398621},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6433119773864746},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.6344156861305237},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6339973211288452},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5844592452049255},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5491507053375244},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4558696448802948},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4517563581466675},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.4473414719104767},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4336795210838318},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.41080984473228455}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7961558103561401},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7515701055526733},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6802269816398621},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6433119773864746},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.6344156861305237},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6339973211288452},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5844592452049255},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5491507053375244},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4558696448802948},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4517563581466675},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.4473414719104767},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4336795210838318},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.41080984473228455},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2018.2880096","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2018.2880096","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W199797433","https://openalex.org/W242805321","https://openalex.org/W1560076686","https://openalex.org/W1572981833","https://openalex.org/W1686810756","https://openalex.org/W1964127768","https://openalex.org/W1967336819","https://openalex.org/W1994634851","https://openalex.org/W2031708389","https://openalex.org/W2033453961","https://openalex.org/W2035866593","https://openalex.org/W2055983636","https://openalex.org/W2058164556","https://openalex.org/W2059639989","https://openalex.org/W2062520372","https://openalex.org/W2067813398","https://openalex.org/W2096642693","https://openalex.org/W2116076678","https://openalex.org/W2141572457","https://openalex.org/W2150489380","https://openalex.org/W2169232245","https://openalex.org/W2181231176","https://openalex.org/W2256362396","https://openalex.org/W2262882992","https://openalex.org/W2318631040","https://openalex.org/W2320044133","https://openalex.org/W2417256080","https://openalex.org/W2493548801","https://openalex.org/W2507402573","https://openalex.org/W2511363568","https://openalex.org/W2525668722","https://openalex.org/W2551055131","https://openalex.org/W2588478576","https://openalex.org/W2589036197","https://openalex.org/W2606629906","https://openalex.org/W2608568867","https://openalex.org/W2618422320","https://openalex.org/W2624257031","https://openalex.org/W2624386319","https://openalex.org/W2741253082","https://openalex.org/W2755190242","https://openalex.org/W2757028014","https://openalex.org/W2759692151","https://openalex.org/W2765166010","https://openalex.org/W2767970498","https://openalex.org/W2768086131","https://openalex.org/W2768795283","https://openalex.org/W4300179783","https://openalex.org/W6608068731","https://openalex.org/W6674909129","https://openalex.org/W6723609742","https://openalex.org/W6746254105"],"related_works":["https://openalex.org/W3179968364","https://openalex.org/W1999612375","https://openalex.org/W4293226380","https://openalex.org/W2938107654","https://openalex.org/W4390516098","https://openalex.org/W2151749779","https://openalex.org/W3008587939","https://openalex.org/W2969228573","https://openalex.org/W3173456895","https://openalex.org/W4382050342"],"abstract_inverted_index":{"Moving":[0],"object":[1],"detection":[2],"(MOD)":[3],"in":[4,162,187],"videos":[5],"is":[6,14,60,91,106,123,176],"a":[7,30,63,102],"challenging":[8],"task.":[9],"Estimation":[10],"of":[11,65,119,139,141,149,164],"accurate":[12],"background":[13,46,82],"the":[15,19,45,50,56,74,81,88,94,110,113,120,147,150,156,173,181],"key":[16],"to":[17,43,48,108],"extracting":[18],"foreground":[20,40,51,111],"from":[21,52,112],"video":[22,53,59,67,96],"frames.":[23,54],"In":[24],"this":[25],"paper,":[26],"we":[27],"have":[28],"proposed":[29,71,107,121,151,174],"novel":[31],"compact":[32,103,178],"end-to-end":[33],"convolutional":[34],"neural":[35],"network":[36,41,72,105,175],"architecture,":[37],"motion":[38],"saliency":[39,89,115],"(MSFgNet),":[42],"estimate":[44],"and":[47,79,98,131,143,146,167,179],"extract":[49,109],"Initially,":[55],"long":[57],"streaming":[58],"divided":[61],"into":[62],"number":[64,140],"small":[66],"streams":[68],"(SVS).":[69],"The":[70,117,135],"takes":[73],"SVS":[75],"as":[76],"an":[77],"input":[78],"estimates":[80],"frame":[83,97],"for":[84,133,160,185],"each":[85],"SVS.":[86],"Second,":[87],"map":[90],"extracted":[92],"using":[93],"current":[95],"estimated":[99,114],"background.":[100],"Furthermore,":[101],"encoder-decoder":[104],"maps.":[116],"performance":[118,148],"MSFgNet":[122,152],"tested":[124],"on":[125],"three":[126],"benchmark":[127],"datasets":[128],"(CDnet-2014,":[129],"LASIESTA,":[130],"PTIS)":[132],"MOD.":[134],"computational":[136],"complexity":[137],"(handling":[138],"parameters":[142],"execution":[144],"time)":[145],"are":[153],"compared":[154],"with":[155],"existing":[157,182],"state-of-the-art":[158,183],"methods":[159,184],"MOD":[161,186],"terms":[163],"precision,":[165],"recall,":[166],"F-measure.":[168],"Performance":[169],"analysis":[170],"shows":[171],"that":[172],"very":[177],"outperforms":[180],"videos.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":26},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":22},{"year":2019,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
