{"id":"https://openalex.org/W2979156252","doi":"https://doi.org/10.1109/ijcnn.2019.8852022","title":"Two-stage Unsupervised Video Anomaly Detection using Low-rank based Unsupervised One-class Learning with Ridge Regression","display_name":"Two-stage Unsupervised Video Anomaly Detection using Low-rank based Unsupervised One-class Learning with Ridge Regression","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2979156252","doi":"https://doi.org/10.1109/ijcnn.2019.8852022","mag":"2979156252"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852022","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5036028581","display_name":"Jingtao Hu","orcid":"https://orcid.org/0000-0003-0836-2973"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingtao Hu","raw_affiliation_strings":["School of Computer, National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069681054","display_name":"En Zhu","orcid":"https://orcid.org/0000-0003-2305-7555"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"En Zhu","raw_affiliation_strings":["School of Computer, National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100420080","display_name":"Siqi Wang","orcid":"https://orcid.org/0000-0002-8134-9508"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siqi Wang","raw_affiliation_strings":["School of Computer, National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100656645","display_name":"Siwei Wang","orcid":"https://orcid.org/0000-0001-9517-262X"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siwei Wang","raw_affiliation_strings":["School of Computer, National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101727888","display_name":"Xinwang Liu","orcid":"https://orcid.org/0000-0001-9066-1475"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinwang Liu","raw_affiliation_strings":["School of Computer, National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089193327","display_name":"Jianping Yin","orcid":"https://orcid.org/0000-0002-5474-4764"},"institutions":[{"id":"https://openalex.org/I2799850029","display_name":"Dongguan University of Technology","ror":"https://ror.org/01m8p7q42","country_code":"CN","type":"education","lineage":["https://openalex.org/I2799850029"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianping Yin","raw_affiliation_strings":["School of Computer Dongguan, University of Technology, Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Dongguan, University of Technology, Guangdong, China","institution_ids":["https://openalex.org/I2799850029"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.996999979019165,"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/T12391","display_name":"Artificial Immune Systems Applications","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6541481614112854},{"id":"https://openalex.org/keywords/ridge","display_name":"Ridge","score":0.6134201884269714},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6058616638183594},{"id":"https://openalex.org/keywords/stage","display_name":"Stage (stratigraphy)","score":0.5984278917312622},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5689682960510254},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5442581176757812},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5175039768218994},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5073599219322205},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4942319691181183},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.43049055337905884},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3457825779914856},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1902095079421997},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18081074953079224},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.14352816343307495}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6541481614112854},{"id":"https://openalex.org/C32277403","wikidata":"https://www.wikidata.org/wiki/Q740445","display_name":"Ridge","level":2,"score":0.6134201884269714},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6058616638183594},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.5984278917312622},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5689682960510254},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5442581176757812},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5175039768218994},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5073599219322205},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4942319691181183},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.43049055337905884},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3457825779914856},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1902095079421997},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18081074953079224},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.14352816343307495},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852022","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6200000047683716}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1520757484","https://openalex.org/W1536680647","https://openalex.org/W1560724230","https://openalex.org/W1967456674","https://openalex.org/W1974879849","https://openalex.org/W1991251598","https://openalex.org/W2012931101","https://openalex.org/W2062118960","https://openalex.org/W2112545207","https://openalex.org/W2122361470","https://openalex.org/W2122646361","https://openalex.org/W2125309548","https://openalex.org/W2132870739","https://openalex.org/W2134670479","https://openalex.org/W2138092272","https://openalex.org/W2155541015","https://openalex.org/W2156017603","https://openalex.org/W2163605009","https://openalex.org/W2163612318","https://openalex.org/W2163991508","https://openalex.org/W2164261375","https://openalex.org/W2164489414","https://openalex.org/W2341058432","https://openalex.org/W2519730330","https://openalex.org/W2520489243","https://openalex.org/W2524280059","https://openalex.org/W2533858083","https://openalex.org/W2609175385","https://openalex.org/W2766042539","https://openalex.org/W2777288981","https://openalex.org/W2777342313","https://openalex.org/W2782585111","https://openalex.org/W2962791923","https://openalex.org/W2963610939","https://openalex.org/W2963899855","https://openalex.org/W2964232409","https://openalex.org/W4229504449","https://openalex.org/W4294375521","https://openalex.org/W6631064309","https://openalex.org/W6631782140","https://openalex.org/W6679792166","https://openalex.org/W6682778277","https://openalex.org/W6684191040","https://openalex.org/W6747286410"],"related_works":["https://openalex.org/W4242029529","https://openalex.org/W2361826657","https://openalex.org/W2369419402","https://openalex.org/W2966404314","https://openalex.org/W4285233543","https://openalex.org/W3148196241","https://openalex.org/W1606169643","https://openalex.org/W4230838436","https://openalex.org/W1977912248","https://openalex.org/W3092773549"],"abstract_inverted_index":{"Video":[0],"anomaly":[1,54,101,120,230],"detection":[2,72,102,231],"is":[3,215],"a":[4,32,56,61],"valuable":[5],"but":[6],"challenging":[7,208],"task,":[8],"especially":[9],"in":[10,60,115,227],"the":[11,24,27,38,43,79,87,116,147,171,174,180,186,193,200,224,228],"field":[12],"of":[13,45,90,106,118,126,132,150],"surveillance":[14],"videos":[15],"for":[16],"public":[17],"safety.":[18],"Almost":[19],"all":[20,91],"existing":[21],"methods":[22,226],"tackle":[23],"problem":[25],"under":[26],"supervised":[28,238],"setting":[29],"and":[30,198,233],"only":[31],"few":[33],"attempts":[34],"are":[35,183],"conducted":[36,205],"on":[37,206],"unsupervised":[39,63,67,107,229],"learning.":[40],"To":[41],"avoid":[42,156],"cost":[44],"labeling":[46],"training":[47],"videos,":[48],"this":[49,95],"paper":[50],"proposes":[51],"to":[52,73,155,163,191,219,223],"discriminate":[53],"by":[55,85],"novel":[57],"two-stage":[58],"framework":[59],"fully":[62],"manner.":[64],"Unlike":[65],"previous":[66],"approaches":[68],"using":[69],"local":[70],"change":[71],"discover":[74],"abnormality,":[75],"our":[76,97,123,133,161,213],"method":[77,98,124,162,214],"enjoys":[78],"global":[80],"information":[81],"from":[82,168,179],"video":[83,92,100,119,177,209],"context":[84],"considering":[86],"pair-wise":[88],"similarity":[89],"events.":[93],"In":[94,170],"way,":[96],"formulates":[99],"as":[103],"an":[104],"extension":[105],"one-class":[108,187],"learning,":[109],"which":[110,159],"has":[111],"not":[112],"been":[113],"explored":[114],"literature":[117],"detection.":[121],"Specifically,":[122],"consists":[125],"two":[127,207],"stages:":[128],"The":[129,202],"first":[130,181],"stage":[131,182],"kernel-based":[134],"method,":[135],"named":[136],"Low-rank":[137],"based":[138],"Unsupervised":[139],"One-class":[140],"Learning":[141],"with":[142,152],"Ridge":[143],"Regression":[144],"(LR-UOCL-RR),":[145],"reformulates":[146],"optimization":[148],"goal":[149],"UOCL":[151],"ridge":[153],"regression":[154],"expensive":[157],"computation,":[158],"enables":[160],"handle":[164],"massive":[165],"unlabeled":[166],"data":[167],"videos.":[169],"second":[172],"stage,":[173],"estimated":[175],"normal":[176,196],"events":[178,197],"fed":[184],"into":[185],"support":[188],"vector":[189],"machine":[190],"refine":[192],"profile":[194],"around":[195],"enhance":[199],"performance.":[201],"experimental":[203],"results":[204],"benchmarks":[210],"indicate":[211],"that":[212],"considerably":[216],"superior,":[217],"up":[218],"15:7%":[220],"AUC":[221],"gain,":[222],"state-of-the-art":[225],"task":[232],"even":[234],"better":[235],"than":[236],"several":[237],"approaches.":[239]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}