{"id":"https://openalex.org/W2753526808","doi":"https://doi.org/10.1109/icme.2017.8019325","title":"Remembering history with convolutional LSTM for anomaly detection","display_name":"Remembering history with convolutional LSTM for anomaly detection","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2753526808","doi":"https://doi.org/10.1109/icme.2017.8019325","mag":"2753526808"},"language":"en","primary_location":{"id":"doi:10.1109/icme.2017.8019325","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2017.8019325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia and Expo (ICME)","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/A5013605789","display_name":"Weixin Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weixin Luo","raw_affiliation_strings":["Shanghaitech University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghaitech University, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100320278","display_name":"Wen Liu","orcid":"https://orcid.org/0000-0002-3867-1825"},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Liu","raw_affiliation_strings":["Shanghaitech University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghaitech University, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034339267","display_name":"Shenghua Gao","orcid":"https://orcid.org/0000-0003-1626-2040"},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shenghua Gao","raw_affiliation_strings":["Shanghaitech University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghaitech University, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5013605789"],"corresponding_institution_ids":["https://openalex.org/I30809798"],"apc_list":null,"apc_paid":null,"fwci":24.2944,"has_fulltext":false,"cited_by_count":580,"citation_normalized_percentile":{"value":0.99576615,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"439","last_page":"444"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9973000288009644,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9879999756813049,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8024582266807556},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7683783173561096},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7156452536582947},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.660554826259613},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6538223028182983},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6421226263046265},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.588263988494873},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5700539350509644},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5398916006088257},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5308939814567566},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4836093485355377},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.45988452434539795},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.44708117842674255},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.36436599493026733},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36038661003112793}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8024582266807556},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7683783173561096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7156452536582947},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.660554826259613},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6538223028182983},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6421226263046265},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.588263988494873},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5700539350509644},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5398916006088257},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5308939814567566},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4836093485355377},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.45988452434539795},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.44708117842674255},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.36436599493026733},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36038661003112793},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme.2017.8019325","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2017.8019325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W1485009520","https://openalex.org/W1522734439","https://openalex.org/W1533861849","https://openalex.org/W1534763723","https://openalex.org/W1536680647","https://openalex.org/W1568514080","https://openalex.org/W1947481528","https://openalex.org/W1983364832","https://openalex.org/W2004149082","https://openalex.org/W2021659075","https://openalex.org/W2024868105","https://openalex.org/W2095640719","https://openalex.org/W2122361470","https://openalex.org/W2155893237","https://openalex.org/W2163605009","https://openalex.org/W2163612318","https://openalex.org/W2164261375","https://openalex.org/W2270470215","https://openalex.org/W2341058432","https://openalex.org/W2342662179","https://openalex.org/W2519730330","https://openalex.org/W2533739470","https://openalex.org/W2962791923","https://openalex.org/W4294306266","https://openalex.org/W6628877408","https://openalex.org/W6631943919","https://openalex.org/W6632082525","https://openalex.org/W6684191040","https://openalex.org/W6693869516","https://openalex.org/W6728658156","https://openalex.org/W6990529707","https://openalex.org/W7015296915"],"related_works":["https://openalex.org/W4394785709","https://openalex.org/W4296978181","https://openalex.org/W2912987408","https://openalex.org/W2937381246","https://openalex.org/W3004801820","https://openalex.org/W4281672036","https://openalex.org/W4313444753","https://openalex.org/W4230582276","https://openalex.org/W3186512740","https://openalex.org/W3194885736"],"abstract_inverted_index":{"This":[0],"paper":[1],"tackles":[2],"anomaly":[3,14,88,149,159],"detection":[4],"in":[5,94],"videos,":[6],"which":[7,50,64,101],"is":[8,15,65],"an":[9],"extremely":[10],"challenging":[11],"task":[12,20],"because":[13],"unbounded.":[16],"We":[17],"approach":[18],"this":[19],"by":[21],"leveraging":[22,37],"a":[23,38,98,123],"Convolutional":[24,39,85],"Neural":[25],"Network":[26],"(CNN":[27],"or":[28],"ConvNet)":[29],"for":[30,33,45,78,110,158],"appearance":[31,75,107,142],"encoding":[32],"each":[34],"frame,":[35],"and":[36,60,76,108,130,143],"Long":[40],"Short":[41],"Term":[42],"Memory":[43],"(ConvLSTM)":[44],"memorizing":[46],"all":[47],"past":[48],"frames":[49],"corresponds":[51],"to":[52,67,70],"the":[53,72,79,104,139,153],"motion":[54,77,109],"information.":[55],"Then":[56],"we":[57,96,118],"integrate":[58],"ConvNet":[59],"ConvLSTM":[61],"with":[62,83],"Auto-Encoder,":[63],"referred":[66],"as":[68],"ConvLSTM-AE,":[69],"learn":[71],"regularity":[73],"of":[74,106,141,155],"ordinary":[80],"moments.":[81],"Compared":[82],"3D":[84],"Auto-Encoder":[86],"based":[87],"detection,":[89],"our":[90,116,134,156],"main":[91],"contribution":[92],"lies":[93],"that":[95,133],"propose":[97],"ConvLSTM-AE":[99],"framework":[100],"better":[102],"encodes":[103],"change":[105,140],"normal":[111],"events,":[112],"respectively.":[113],"To":[114],"evaluate":[115],"method,":[117],"first":[119],"conduct":[120],"experiments":[121,146],"on":[122,147],"synthesized":[124],"Moving-MNIST":[125],"dataset":[126],"under":[127],"controlled":[128],"settings,":[129],"results":[131],"show":[132],"method":[135,157],"can":[136],"easily":[137],"identify":[138],"motion.":[144],"Extensive":[145],"real":[148],"datasets":[150],"further":[151],"validate":[152],"effectiveness":[154],"detection.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":55},{"year":2024,"cited_by_count":91},{"year":2023,"cited_by_count":102},{"year":2022,"cited_by_count":109},{"year":2021,"cited_by_count":98},{"year":2020,"cited_by_count":57},{"year":2019,"cited_by_count":39},{"year":2018,"cited_by_count":20},{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
