{"id":"https://openalex.org/W3212711973","doi":"https://doi.org/10.1109/access.2021.3126335","title":"An Approach to Detect Anomaly in Video Using Deep Generative Network","display_name":"An Approach to Detect Anomaly in Video Using Deep Generative Network","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3212711973","doi":"https://doi.org/10.1109/access.2021.3126335","mag":"3212711973"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3126335","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_indexed_in_scopus":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"journal-article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2021.3126335","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082217250","display_name":"Savath Saypadith","orcid":"https://orcid.org/0000-0001-7101-8257"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Savath Saypadith","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5061693379","display_name":"Takao Onoye","orcid":"https://orcid.org/0000-0002-1894-2448"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"Osaka University","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takao Onoye","raw_affiliation_strings":["Graduate School of Information Science and Technology, Osaka University, Japan."],"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, Osaka University, Japan.","institution_ids":["https://openalex.org/I98285908"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850,"provenance":"doaj"},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850,"provenance":"doaj"},"fwci":1.225,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.684384,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":89},"biblio":{"volume":"9","issue":null,"first_page":"150903","last_page":"150910"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9972,"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":0.9909,"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/benchmark","display_name":"Benchmark (surveying)","score":0.7154999},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5715102}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8422468},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7544179},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7154999},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6030608},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5715102},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5001223},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.46732062},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45300496},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44576374},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44214624},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39194676},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37216488},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.18787846},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.10039461},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3126335","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_indexed_in_scopus":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},{"is_oa":false,"landing_page_url":"https://doaj.org/article/106c733f24d94516a46ccfc178e106ca","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3126335","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_indexed_in_scopus":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[{"score":0.46,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"grants":[{"funder":"https://openalex.org/F4320322716","funder_display_name":"Japan International Cooperation Agency","award_id":null}],"datasets":[],"versions":[],"referenced_works_count":43,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1522734439","https://openalex.org/W1799366690","https://openalex.org/W1901129140","https://openalex.org/W1959608418","https://openalex.org/W1983364832","https://openalex.org/W2016053056","https://openalex.org/W2064675550","https://openalex.org/W2097117768","https://openalex.org/W2099471712","https://openalex.org/W2122361470","https://openalex.org/W2122646361","https://openalex.org/W2163612318","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2341058432","https://openalex.org/W2401231614","https://openalex.org/W2402144811","https://openalex.org/W2471801048","https://openalex.org/W2565639579","https://openalex.org/W2579718262","https://openalex.org/W2753526808","https://openalex.org/W2753738274","https://openalex.org/W2777342313","https://openalex.org/W2901629142","https://openalex.org/W2953384591","https://openalex.org/W2962791923","https://openalex.org/W2963073614","https://openalex.org/W2963610939","https://openalex.org/W2963795951","https://openalex.org/W2963878592","https://openalex.org/W2964032056","https://openalex.org/W2964137095","https://openalex.org/W2964191259","https://openalex.org/W2964350391","https://openalex.org/W2999972254","https://openalex.org/W3015832418","https://openalex.org/W3107664750","https://openalex.org/W3122625936","https://openalex.org/W3157370245","https://openalex.org/W4320013936","https://openalex.org/W764651262"],"related_works":["https://openalex.org/W972276598","https://openalex.org/W4321353415","https://openalex.org/W4283314094","https://openalex.org/W4246352526","https://openalex.org/W4230315250","https://openalex.org/W2745001401","https://openalex.org/W2378211422","https://openalex.org/W2130974462","https://openalex.org/W2086519370","https://openalex.org/W2028665553"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,45,73,134,275],"in":[2,13,29,59,74,131,208,250,262],"the":[3,14,20,23,30,33,36,40,43,68,94,99,102,107,123,137,140,147,154,158,180,190,194,200,209,232,236,255,269,274],"video":[4,75],"has":[5,26,113,215],"recently":[6],"gained":[7],"attention":[8],"due":[9],"to":[10,56,93,97,121,145,152,161,268],"its":[11],"importance":[12],"intelligent":[15],"surveillance":[16],"system.":[17],"Even":[18],"though":[19],"performance":[21,129],"of":[22,42,101,106,117,125,133,170,206,252,257],"state-of-art":[24],"methods":[25,239],"been":[27,114,216],"competitive":[28],"benchmark":[31,220],"dataset,":[32],"trade-off":[34],"between":[35],"computational":[37],"resource":[38],"and":[39,87,104,150,157,197,227,248,259],"accuracy":[41,276],"anomaly":[44,72],"should":[46],"be":[47,162],"considered.":[48],"In":[49,136],"this":[50],"paper,":[51],"we":[52],"present":[53],"a":[54,63,230],"framework":[55,214,234,264],"detect":[57],"anomalies":[58,207],"video.":[60],"We":[61],"proposed":[62,213,233],"\u201cmulti-scale":[64],"U-Net\u201d":[65],"network":[66,80,96,142,185,271],"architecture,":[67,272],"unsupervised":[69],"learning":[70],"for":[71],"based":[76],"on":[77,193,218,240],"generative":[78],"adversarial":[79],"(GAN)":[81],"structure.":[82],"Shortcut":[83],"Inception":[84],"Modules":[85],"(SIMs)":[86],"residual":[88],"skip":[89],"connection":[90],"are":[91,265],"employed":[92],"generator":[95,141,184],"increase":[98],"ability":[100],"training":[103,126,138,177,258],"testing":[105,210,260],"neural":[108],"network.":[109],"An":[110],"asymmetric":[111],"convolution":[112,119,181],"applied":[115],"instead":[116],"traditional":[118],"layers":[120],"decrease":[122],"number":[124],"parameters":[127,261],"without":[128],"penalty":[130],"terms":[132,251],"accuracy.":[135],"phase,":[139],"was":[143],"trained":[144,187],"generate":[146],"normal":[148,195],"events":[149],"attempt":[151],"make":[153],"generated":[155],"image":[156,172],"ground":[159],"truth":[160],"similar.":[163],"A":[164],"multi-scale":[165],"U-Net":[166],"kept":[167],"useful":[168],"features":[169],"an":[171,204],"that":[173],"were":[174],"lost":[175],"during":[176],"caused":[178],"by":[179,188],"operator.":[182],"The":[183],"is":[186,277],"minimizing":[189],"reconstruction":[191,201],"error":[192,202],"data":[196],"then":[198],"using":[199],"as":[203],"indicator":[205],"phase.":[211],"Our":[212],"evaluated":[217],"three":[219],"datasets,":[221,243],"including":[222],"UCSD":[223],"pedestrian,":[224],"CHUK":[225],"Avenue,":[226],"ShanghaiTech.":[228],"As":[229],"result,":[231],"surpasses":[235],"state-of-the-art":[237],"learning-based":[238],"all":[241],"these":[242],"which":[244],"achieved":[245],"95.7%,":[246],"86.9%,":[247],"73.0%":[249],"AUC.":[253],"Moreover,":[254],"numbers":[256],"our":[263],"reduced":[266],"compared":[267],"baseline":[270],"while":[273],"still":[278],"improved.":[279]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3212711973","counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3}],"updated_date":"2025-01-21T23:19:52.036875","created_date":"2021-11-22"}