{"id":"https://openalex.org/W2789351528","doi":"https://doi.org/10.1109/vcip.2017.8305025","title":"Adaptive difference modelling for background subtraction","display_name":"Adaptive difference modelling for background subtraction","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2789351528","doi":"https://doi.org/10.1109/vcip.2017.8305025","mag":"2789351528"},"language":"en","primary_location":{"id":"doi:10.1109/vcip.2017.8305025","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2017.8305025","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Visual Communications and Image Processing (VCIP)","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/A5047237045","display_name":"Xianghao Zang","orcid":"https://orcid.org/0000-0001-8421-7167"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xianghao Zang","raw_affiliation_strings":["Digital Media R&D Center, Shenzhen Graduate School"],"affiliations":[{"raw_affiliation_string":"Digital Media R&D Center, Shenzhen Graduate School","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076676389","display_name":"Ge Li","orcid":"https://orcid.org/0000-0003-4079-3968"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ge Li","raw_affiliation_strings":["Digital Media R&D Center, Shenzhen Graduate School"],"affiliations":[{"raw_affiliation_string":"Digital Media R&D Center, Shenzhen Graduate School","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101912623","display_name":"Jun Yang","orcid":"https://orcid.org/0000-0002-9386-5825"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jun Yang","raw_affiliation_strings":["Digital Media R&D Center, Shenzhen Graduate School"],"affiliations":[{"raw_affiliation_string":"Digital Media R&D Center, Shenzhen Graduate School","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017052768","display_name":"Wenmin Wang","orcid":"https://orcid.org/0000-0003-2664-4413"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenmin Wang","raw_affiliation_strings":["Digital Media R&D Center, Shenzhen Graduate School"],"affiliations":[{"raw_affiliation_string":"Digital Media R&D Center, Shenzhen Graduate School","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5047237045"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2731,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.66137085,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"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/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"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9955999851226807,"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":0.9084291458129883},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.726487934589386},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7053731679916382},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.6445476412773132},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6429228186607361},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5989526510238647},{"id":"https://openalex.org/keywords/temporal-difference-learning","display_name":"Temporal difference learning","score":0.5772889852523804},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5558279752731323},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5207086205482483},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.48587220907211304},{"id":"https://openalex.org/keywords/shake","display_name":"Shake","score":0.42182719707489014},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.408670574426651},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3950466513633728}],"concepts":[{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.9084291458129883},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.726487934589386},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7053731679916382},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.6445476412773132},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6429228186607361},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5989526510238647},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.5772889852523804},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5558279752731323},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5207086205482483},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.48587220907211304},{"id":"https://openalex.org/C2779053110","wikidata":"https://www.wikidata.org/wiki/Q7462601","display_name":"Shake","level":2,"score":0.42182719707489014},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.408670574426651},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3950466513633728},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","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},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip.2017.8305025","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2017.8305025","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1573546770","https://openalex.org/W1964127768","https://openalex.org/W1969977005","https://openalex.org/W1994634851","https://openalex.org/W2071860582","https://openalex.org/W2083245309","https://openalex.org/W2102625004","https://openalex.org/W2127070222","https://openalex.org/W2130293653","https://openalex.org/W2140235142","https://openalex.org/W2141572457","https://openalex.org/W2151928481","https://openalex.org/W2489049096","https://openalex.org/W2512785428","https://openalex.org/W3101795426","https://openalex.org/W4231180850","https://openalex.org/W4248936881","https://openalex.org/W6884942452"],"related_works":["https://openalex.org/W2357124094","https://openalex.org/W2387399993","https://openalex.org/W2389739210","https://openalex.org/W2348924972","https://openalex.org/W2365736347","https://openalex.org/W1967490298","https://openalex.org/W2047454415","https://openalex.org/W2789351528","https://openalex.org/W1967417841","https://openalex.org/W2159008857"],"abstract_inverted_index":{"Background":[0,100],"subtraction":[1],"plays":[2],"a":[3,47],"very":[4],"important":[5],"role":[6],"in":[7,11,42],"video":[8],"analysis,":[9],"especially":[10],"surveillance":[12],"systems.":[13],"While":[14],"being":[15],"straightforward,":[16],"the":[17,61,75,91,99],"performance":[18],"based":[19],"on":[20,95,110],"frame":[21],"differencing":[22],"is":[23],"unsatisfied":[24],"due":[25],"to":[26,29,59,72,78,88],"its":[27,40],"sensitiveness":[28],"issues":[30],"such":[31],"as":[32],"camera":[33],"shake":[34],"and":[35],"swinging":[36],"objects.":[37],"To":[38],"address":[39],"limitations,":[41],"this":[43],"paper":[44],"we":[45,54,67],"propose":[46],"complete":[48],"adaptive":[49],"difference":[50,57,76],"modelling":[51],"framework.":[52],"First,":[53],"introduce":[55],"two":[56],"discriminators":[58],"model":[60,92],"evolution":[62],"process":[63],"of":[64,98],"pixels.":[65],"Second,":[66],"use":[68],"Gaussian":[69],"Mixture":[70],"Models":[71,101],"adaptively":[73],"learn":[74],"threshold":[77],"distinguish":[79],"foreground":[80],"from":[81],"background.":[82],"Third,":[83],"three":[84],"heuristics":[85],"are":[86],"employed":[87],"further":[89],"improve":[90],"adaptability.":[93],"Experiments":[94],"real-world":[96],"videos":[97],"Challenge":[102],"(BMC)":[103],"demonstrate":[104],"that":[105],"our":[106],"method":[107],"performs":[108],"better":[109],"global":[111],"quality":[112],"metric":[113],"(FSD)":[114],"than":[115],"other":[116],"state-of-the-art":[117],"methods.":[118]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
