{"id":"https://openalex.org/W2140329186","doi":"https://doi.org/10.1109/mmsp.2008.4665205","title":"A hybrid object detection technique from dynamic background using Gaussian mixture models","display_name":"A hybrid object detection technique from dynamic background using Gaussian mixture models","publication_year":2008,"publication_date":"2008-10-01","ids":{"openalex":"https://openalex.org/W2140329186","doi":"https://doi.org/10.1109/mmsp.2008.4665205","mag":"2140329186"},"language":"en","primary_location":{"id":"doi:10.1109/mmsp.2008.4665205","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2008.4665205","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://researchoutput.csu.edu.au/en/publications/f918aea9-b70a-4ec5-ab1b-df04e427b085","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100859810","display_name":"Mahfuzul Haque","orcid":null},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Mahfuzul Haque","raw_affiliation_strings":["Gippsland School of Information Technology, Monash University, VIC, Australia","Gippsland Sch. of Inf. Technol., Monash Univ., Clayton, Vic"],"affiliations":[{"raw_affiliation_string":"Gippsland School of Information Technology, Monash University, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]},{"raw_affiliation_string":"Gippsland Sch. of Inf. Technol., Monash Univ., Clayton, Vic","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010357798","display_name":"Manzur Murshed","orcid":"https://orcid.org/0000-0001-7079-9717"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Manzur Murshed","raw_affiliation_strings":["Gippsland School of Information Technology, Monash University, VIC, Australia","Gippsland Sch. of Inf. Technol., Monash Univ., Clayton, Vic"],"affiliations":[{"raw_affiliation_string":"Gippsland School of Information Technology, Monash University, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]},{"raw_affiliation_string":"Gippsland Sch. of Inf. Technol., Monash Univ., Clayton, Vic","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002818315","display_name":"Manoranjan Paul","orcid":"https://orcid.org/0000-0001-6870-5056"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Manoranjan Paul","raw_affiliation_strings":["Gippsland School of Information Technology, Monash University, VIC, Australia","Gippsland Sch. of Inf. Technol., Monash Univ., Clayton, Vic"],"affiliations":[{"raw_affiliation_string":"Gippsland School of Information Technology, Monash University, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]},{"raw_affiliation_string":"Gippsland Sch. of Inf. Technol., Monash Univ., Clayton, Vic","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100859810"],"corresponding_institution_ids":["https://openalex.org/I56590836"],"apc_list":null,"apc_paid":null,"fwci":1.0649,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.80782262,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"5308","issue":null,"first_page":"915","last_page":"920"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9934999942779541,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9782000184059143,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/background-subtraction","display_name":"Background subtraction","score":0.8883844614028931},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7904030084609985},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6780305504798889},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6504217386245728},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.5609860420227051},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5536500215530396},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5083021521568298},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4705139696598053},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44388338923454285},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4243535101413727},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.42313721776008606},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38402000069618225},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.373543918132782},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.1501568853855133},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08461630344390869}],"concepts":[{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.8883844614028931},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7904030084609985},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6780305504798889},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6504217386245728},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.5609860420227051},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5536500215530396},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5083021521568298},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4705139696598053},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44388338923454285},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4243535101413727},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42313721776008606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38402000069618225},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.373543918132782},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.1501568853855133},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08461630344390869},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1109/mmsp.2008.4665205","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2008.4665205","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/f918aea9-b70a-4ec5-ab1b-df04e427b085","is_oa":true,"landing_page_url":"https://researchoutput.csu.edu.au/en/publications/f918aea9-b70a-4ec5-ab1b-df04e427b085","pdf_url":null,"source":{"id":"https://openalex.org/S7407055442","display_name":"Charles Sturt University Research Output (CRO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Haque, M, Murshed, M & Paul, M 2008, A Hybrid Object Detection Technique from Dynamic Background Using Gaussian Mixture Models. in IEEE Workshop on Multimedia Signal Processing (MMSP). IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 915-920, MMSP 2008: 10th Workshop, Australia, 08/10/08. https://doi.org/10.1109/MMSP.2008.4665205","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:vital:6270","is_oa":false,"landing_page_url":"http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/41337","pdf_url":null,"source":{"id":"https://openalex.org/S4306400234","display_name":"FedUni ResearchOnline (Federation University Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210158496","host_organization_name":"Australian Federation of University Women \u2013 South Australia","host_organization_lineage":["https://openalex.org/I4210158496"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.721.7301","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.721.7301","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://csusap.csu.edu.au/%7Erpaul/paper/Mmsp2008.pdf","raw_type":"text"},{"id":"pmh:oai:figshare.com:article/22028534","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/A_hybrid_object_detection_technique_from_dynamic_background_using_Gaussian_mixture_models/22028534","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"pmh:oai:figshare.com:article/22028537","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/A_Hybrid_Object_Detection_Technique_from_Dynamic_Background_Using_Gaussian_Mixture_Models/22028537","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:publications/f918aea9-b70a-4ec5-ab1b-df04e427b085","is_oa":true,"landing_page_url":"https://researchoutput.csu.edu.au/en/publications/f918aea9-b70a-4ec5-ab1b-df04e427b085","pdf_url":null,"source":{"id":"https://openalex.org/S7407055442","display_name":"Charles Sturt University Research Output (CRO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Haque, M, Murshed, M & Paul, M 2008, A Hybrid Object Detection Technique from Dynamic Background Using Gaussian Mixture Models. in IEEE Workshop on Multimedia Signal Processing (MMSP). IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 915-920, MMSP 2008: 10th Workshop, Australia, 08/10/08. https://doi.org/10.1109/MMSP.2008.4665205","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W50432363","https://openalex.org/W1573546770","https://openalex.org/W1963524043","https://openalex.org/W1994304612","https://openalex.org/W2101223341","https://openalex.org/W2102625004","https://openalex.org/W2110155975","https://openalex.org/W2115415549","https://openalex.org/W2130260536","https://openalex.org/W2130350775","https://openalex.org/W2148197091","https://openalex.org/W2148290050","https://openalex.org/W2169367125","https://openalex.org/W6602016701","https://openalex.org/W6634202927","https://openalex.org/W6648923259"],"related_works":["https://openalex.org/W2145985110","https://openalex.org/W2554449009","https://openalex.org/W2730449274","https://openalex.org/W2754428891","https://openalex.org/W2547934522","https://openalex.org/W1983333094","https://openalex.org/W2341132039","https://openalex.org/W2188519561","https://openalex.org/W2787282005","https://openalex.org/W2309573947"],"abstract_inverted_index":{"Adaptive":[0],"background":[1,50,62,134],"modelling":[2],"based":[3],"object":[4,111,154,169],"detection":[5,112,155,170],"techniques":[6,93],"are":[7,24,102,143],"widely":[8],"used":[9,129],"in":[10,179],"machine":[11],"vision":[12],"applications":[13,58],"for":[14,56,114,130,145,150],"handling":[15],"the":[16,46,116,152,173,176],"challenges":[17],"of":[18,118,175],"real-world":[19],"multimodal":[20,71],"background.":[21,72],"But":[22],"they":[23],"constrained":[25],"to":[26,30,60,68],"specific":[27,34],"environment":[28,33,99],"due":[29,59],"relying":[31],"on":[32],"parameters,":[35],"and":[36,65,82,88,136,139,163,183],"their":[37],"performances":[38],"also":[39],"fluctuate":[40],"across":[41,78,190],"different":[42,79],"operating":[43,80,192],"speeds.":[44,193],"On":[45],"other":[47],"side,":[48],"basic":[49,140],"subtraction":[51,141],"(BBS)":[52],"is":[53,128],"not":[54],"suitable":[55],"real":[57],"manual":[61],"initialization":[63],"requirement":[64],"its":[66],"inability":[67],"handle":[69],"repetitive":[70],"However,":[73],"it":[74],"shows":[75],"better":[76,84,188],"stability":[77,189],"speeds":[81],"can":[83],"eliminate":[85],"noise,":[86,181],"shadow,":[87,182],"trailing":[89,184],"effect":[90,185],"than":[91],"adaptive":[92,133,168],"as":[94],"no":[95],"model":[96,135],"adaptability":[97],"or":[98],"related":[100],"parameters":[101],"involved.":[103],"In":[104,121],"this":[105],"paper,":[106],"we":[107],"propose":[108],"a":[109],"hybrid":[110],"technique":[113,171,178],"incorporating":[115],"strengths":[117],"both":[119,137],"approaches.":[120],"our":[122],"technique,":[123],"Gaussian":[124],"mixture":[125],"models":[126],"(GMM)":[127],"maintaining":[131,187],"an":[132],"probabilistic":[138],"decisions":[142],"utilized":[144],"calculating":[146],"inexpensive":[147],"neighbourhood":[148],"statistics":[149],"guiding":[151],"final":[153],"decision.":[156],"Experimental":[157],"results":[158],"with":[159,166],"two":[160],"benchmark":[161],"datasets":[162],"comparative":[164],"analysis":[165],"recent":[167],"show":[172],"strength":[174],"proposed":[177],"eliminating":[180],"while":[186],"variable":[191]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
