{"id":"https://openalex.org/W2972156365","doi":"https://doi.org/10.1109/tnnls.2019.2933554","title":"A Deep One-Class Neural Network for Anomalous Event Detection in Complex Scenes","display_name":"A Deep One-Class Neural Network for Anomalous Event Detection in Complex Scenes","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2972156365","doi":"https://doi.org/10.1109/tnnls.2019.2933554","mag":"2972156365","pmid":"https://pubmed.ncbi.nlm.nih.gov/31494560"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2019.2933554","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2019.2933554","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","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/A5032693575","display_name":"Peng Wu","orcid":"https://orcid.org/0000-0003-2938-6798"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Wu","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0003-2938-6798","affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100375038","display_name":"Jing Liu","orcid":"https://orcid.org/0000-0002-6834-5350"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Liu","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0002-6834-5350","affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011477215","display_name":"Fang Shen","orcid":"https://orcid.org/0000-0002-1988-9714"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Shen","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":{"value":2045,"currency":"USD","value_usd":2045},"apc_paid":null,"fwci":13.6885,"has_fulltext":false,"cited_by_count":199,"citation_normalized_percentile":{"value":0.98986673,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"31","issue":"7","first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9781000018119812,"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":0.7947654128074646},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7509726285934448},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7301886081695557},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6320353150367737},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5565462112426758},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.504733681678772},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5001835823059082},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49058762192726135},{"id":"https://openalex.org/keywords/one-class-classification","display_name":"One-class classification","score":0.4166107475757599},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3422069549560547}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7947654128074646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7509726285934448},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7301886081695557},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6320353150367737},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5565462112426758},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.504733681678772},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5001835823059082},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49058762192726135},{"id":"https://openalex.org/C34872919","wikidata":"https://www.wikidata.org/wiki/Q7092302","display_name":"One-class classification","level":3,"score":0.4166107475757599},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3422069549560547}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2019.2933554","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2019.2933554","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:31494560","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31494560","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 neural networks and learning 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":76,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1522734439","https://openalex.org/W1578985305","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W1967456674","https://openalex.org/W1967815760","https://openalex.org/W1983364832","https://openalex.org/W1988115241","https://openalex.org/W2012931101","https://openalex.org/W2021659075","https://openalex.org/W2068678179","https://openalex.org/W2074076772","https://openalex.org/W2100294832","https://openalex.org/W2100495367","https://openalex.org/W2105497548","https://openalex.org/W2118978333","https://openalex.org/W2119821739","https://openalex.org/W2122361470","https://openalex.org/W2125105611","https://openalex.org/W2156303437","https://openalex.org/W2163605009","https://openalex.org/W2163612318","https://openalex.org/W2163922914","https://openalex.org/W2164261375","https://openalex.org/W2164489414","https://openalex.org/W2194775991","https://openalex.org/W2214352687","https://openalex.org/W2221448138","https://openalex.org/W2271840356","https://openalex.org/W2341058432","https://openalex.org/W2503288456","https://openalex.org/W2531279268","https://openalex.org/W2540481276","https://openalex.org/W2606538621","https://openalex.org/W2753526808","https://openalex.org/W2756241593","https://openalex.org/W2756973318","https://openalex.org/W2763384612","https://openalex.org/W2764289073","https://openalex.org/W2777342313","https://openalex.org/W2782585111","https://openalex.org/W2786088545","https://openalex.org/W2797826601","https://openalex.org/W2805931155","https://openalex.org/W2884367402","https://openalex.org/W2904885916","https://openalex.org/W2962835968","https://openalex.org/W2962934715","https://openalex.org/W2963037989","https://openalex.org/W2963061824","https://openalex.org/W2963125871","https://openalex.org/W2963610939","https://openalex.org/W2963795951","https://openalex.org/W2963881378","https://openalex.org/W2963899855","https://openalex.org/W2964074409","https://openalex.org/W2964121744","https://openalex.org/W3015571647","https://openalex.org/W3105939760","https://openalex.org/W4237171445","https://openalex.org/W4239510810","https://openalex.org/W4297772798","https://openalex.org/W6631190155","https://openalex.org/W6634902294","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6682864246","https://openalex.org/W6684191040","https://openalex.org/W6685380521","https://openalex.org/W6691096134","https://openalex.org/W6694517276","https://openalex.org/W6747286410","https://openalex.org/W6748102297","https://openalex.org/W6786035305"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W3171512724","https://openalex.org/W2169365377","https://openalex.org/W4206552806"],"abstract_inverted_index":{"How":[0],"to":[1,9,32,38,49,92,103,131,158,171],"build":[2],"a":[3,33,41,60,77,100,127,205,238],"generic":[4],"deep":[5,46],"one-class":[6,11,29,51,101],"(DeepOC)":[7],"model":[8,180,207],"solve":[10,50],"classification":[12,52],"problems":[13],"for":[14,112],"anomaly":[15,233],"detection,":[16],"such":[17],"as":[18,66,107,109],"anomalous":[19],"event":[20],"detection":[21,234],"in":[22,55,129],"complex":[23],"scenes?":[24],"The":[25],"characteristics":[26],"of":[27,115,174],"existing":[28,240],"labels":[30],"lead":[31],"dilemma:":[34],"it":[35],"is":[36,142,155,204,227],"hard":[37],"directly":[39],"use":[40,87,187],"multiple":[42],"classifier":[43,102],"based":[44],"on":[45,219],"neural":[47,63,216],"networks":[48],"problems.":[53],"Therefore,":[54],"this":[56],"article,":[57],"we":[58,86,125],"propose":[59],"novel":[61],"DeepOC":[62,78,203,226],"network,":[64],"termed":[65],"DeepOC,":[67],"which":[68],"can":[69],"simultaneously":[70],"learn":[71],"compact":[72,108],"feature":[73,122,138],"representations":[74],"and":[75,98,120,168,182,200,229],"train":[76,99],"classifier.":[79],"Only":[80],"with":[81,237],"the":[82,88,113,116,121,150,156,179,231],"given":[83],"normal":[84],"samples,":[85],"stacked":[89],"convolutional":[90],"encoder":[91],"generate":[93],"their":[94],"low-dimensional":[95,137],"high-level":[96],"features":[97,106,189,199,210],"make":[104],"these":[105,136],"possible.":[110],"Meanwhile,":[111],"sake":[114],"correct":[117],"mapping":[118],"relation":[119],"representations'":[123],"diversity,":[124],"utilize":[126],"decoder":[128],"order":[130],"reconstruct":[132],"raw":[133],"samples":[134],"from":[135],"representations.":[139],"This":[140,153],"structure":[141],"gradually":[143],"established":[144],"using":[145,208],"an":[146],"adversarial":[147],"mechanism":[148,154],"during":[149],"training":[151,201],"stage.":[152],"key":[157],"our":[159],"model.":[160],"It":[161],"organically":[162],"combines":[163],"two":[164,196],"seemingly":[165],"contradictory":[166],"components":[167],"allows":[169],"them":[170],"take":[172],"advantage":[173],"each":[175],"other,":[176],"thus":[177],"making":[178],"robust":[181],"effective.":[183],"Unlike":[184],"methods":[185],"that":[186,192,211,225],"handcrafted":[188],"or":[190],"those":[191],"are":[193,212],"separated":[194],"into":[195],"stages":[197],"(extracting":[198],"classifiers),":[202],"one-stage":[206],"reliable":[209],"automatically":[213],"extracted":[214],"by":[215],"networks.":[217],"Experiments":[218],"various":[220],"benchmark":[221],"data":[222],"sets":[223],"show":[224],"feasible":[228],"achieves":[230],"state-of-the-art":[232],"results":[235],"compared":[236],"dozen":[239],"methods.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":33},{"year":2024,"cited_by_count":36},{"year":2023,"cited_by_count":42},{"year":2022,"cited_by_count":36},{"year":2021,"cited_by_count":31},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":4}],"updated_date":"2026-08-29T07:29:34.045763","created_date":"2025-10-10T00:00:00"}
