{"id":"https://openalex.org/W4312328856","doi":"https://doi.org/10.1109/iscas48785.2022.9937339","title":"Joint Representation Learning for Anomaly Detection in Surveillance Videos","display_name":"Joint Representation Learning for Anomaly Detection in Surveillance Videos","publication_year":2022,"publication_date":"2022-05-28","ids":{"openalex":"https://openalex.org/W4312328856","doi":"https://doi.org/10.1109/iscas48785.2022.9937339"},"language":"en","primary_location":{"id":"doi:10.1109/iscas48785.2022.9937339","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas48785.2022.9937339","pdf_url":null,"source":{"id":"https://openalex.org/S4363604393","display_name":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5082217250","display_name":"Savath Saypadith","orcid":"https://orcid.org/0000-0001-7101-8257"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Savath Saypadith","raw_affiliation_strings":["Osaka University,Graduate School of Information Science and Technology,Osaka,Japan","Graduate School of Information Science and Technology, Osaka University, Osaka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka University,Graduate School of Information Science and Technology,Osaka,Japan","institution_ids":["https://openalex.org/I98285908"]},{"raw_affiliation_string":"Graduate School of Information Science and Technology, Osaka University, Osaka, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"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":"The University of Osaka","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":["Osaka University,Graduate School of Information Science and Technology,Osaka,Japan","Graduate School of Information Science and Technology, Osaka University, Osaka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka University,Graduate School of Information Science and Technology,Osaka,Japan","institution_ids":["https://openalex.org/I98285908"]},{"raw_affiliation_string":"Graduate School of Information Science and Technology, Osaka University, Osaka, Japan","institution_ids":["https://openalex.org/I98285908"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2076,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.43901405,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"737","last_page":"741"},"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":0.9944999814033508,"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":0.98089998960495,"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/computer-science","display_name":"Computer science","score":0.8156901597976685},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7906337380409241},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7631405591964722},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.704875648021698},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.6380240321159363},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5997262001037598},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5790243148803711},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5453396439552307},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5256510972976685},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4895082712173462},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4812605082988739},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.46896448731422424},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4662650227546692},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4503936171531677},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.44213518500328064},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.42134302854537964},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4126884937286377},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.4125921130180359},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.29921579360961914},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08279120922088623}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8156901597976685},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7906337380409241},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7631405591964722},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.704875648021698},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.6380240321159363},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5997262001037598},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5790243148803711},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5453396439552307},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5256510972976685},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4895082712173462},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4812605082988739},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.46896448731422424},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4662650227546692},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4503936171531677},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.44213518500328064},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.42134302854537964},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4126884937286377},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.4125921130180359},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.29921579360961914},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08279120922088623},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas48785.2022.9937339","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas48785.2022.9937339","pdf_url":null,"source":{"id":"https://openalex.org/S4363604393","display_name":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8199999928474426}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2021659075","https://openalex.org/W2022199461","https://openalex.org/W2122361470","https://openalex.org/W2163612318","https://openalex.org/W2194775991","https://openalex.org/W2341058432","https://openalex.org/W2399736860","https://openalex.org/W2439881727","https://openalex.org/W2559423697","https://openalex.org/W2560474170","https://openalex.org/W2579718262","https://openalex.org/W2753526808","https://openalex.org/W2777342313","https://openalex.org/W2915683453","https://openalex.org/W2962791923","https://openalex.org/W2963073614","https://openalex.org/W2963610939","https://openalex.org/W2970724283","https://openalex.org/W2981741013","https://openalex.org/W2989705574","https://openalex.org/W2991506670","https://openalex.org/W3015832418","https://openalex.org/W3022606336","https://openalex.org/W3044290927","https://openalex.org/W4297772798","https://openalex.org/W6639824700","https://openalex.org/W6691096134","https://openalex.org/W6729966448"],"related_works":["https://openalex.org/W2624903463","https://openalex.org/W3207051105","https://openalex.org/W2802018156","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W2922437833","https://openalex.org/W4312696271","https://openalex.org/W4223892596","https://openalex.org/W2933098581","https://openalex.org/W2556125083"],"abstract_inverted_index":{"Video":[0],"anomaly":[1,30,44],"detection":[2,116],"in":[3,103,130],"the":[4,52,81,90,114],"unconstrained":[5],"environment":[6],"is":[7,71],"challenging":[8],"due":[9],"to":[10,73,100,120],"various":[11,104],"background":[12],"scenes,":[13],"illuminations,":[14],"and":[15,55,76,97,128,135],"occlusions.":[16],"Recent":[17],"studies":[18],"show":[19],"that":[20],"deep":[21],"learning":[22,40,88],"approaches":[23],"can":[24],"achieve":[25,125],"remarkable":[26,115],"performance":[27],"on":[28,64,109],"video":[29,43],"detection.":[31,45],"In":[32],"this":[33],"paper,":[34],"we":[35],"propose":[36],"a":[37,85],"joint":[38,86],"representation":[39,87],"structure":[41],"for":[42],"The":[46,107],"proposed":[47,91],"architecture":[48,70,92],"extracts":[49],"features":[50,59,99],"from":[51],"object":[53],"appearance":[54,96],"their":[56],"associate":[57],"motion":[58,98],"via":[60],"different":[61],"encoders":[62],"based":[63],"ResNet":[65],"network":[66,69],"architecture.":[67],"Our":[68],"designed":[72],"combine":[74],"spatial":[75],"temporal":[77],"features,":[78],"which":[79,124],"share":[80],"same":[82],"decoder.":[83],"Using":[84],"approach,":[89],"effectively":[93],"learn":[94],"both":[95],"detect":[101],"anomalies":[102],"scene":[105],"scenarios.":[106],"experiments":[108],"three":[110],"benchmark":[111],"datasets":[112],"demonstrate":[113],"accuracy":[117],"with":[118],"respect":[119],"existing":[121],"state-of-the-art":[122],"methods,":[123],"96.5%,":[126],"86.9%,":[127],"73.4%":[129],"UCSD":[131],"Pedestrian,":[132],"CHUK":[133],"Avenue,":[134],"ShanghaiTech":[136],"datasets,":[137],"respectively.":[138]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}