{"id":"https://openalex.org/W2900471873","doi":"https://doi.org/10.1109/tie.2018.2881943","title":"Activity Recognition Using Temporal Optical Flow Convolutional Features and Multilayer LSTM","display_name":"Activity Recognition Using Temporal Optical Flow Convolutional Features and Multilayer LSTM","publication_year":2018,"publication_date":"2018-11-22","ids":{"openalex":"https://openalex.org/W2900471873","doi":"https://doi.org/10.1109/tie.2018.2881943","mag":"2900471873"},"language":"en","primary_location":{"id":"doi:10.1109/tie.2018.2881943","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tie.2018.2881943","pdf_url":null,"source":{"id":"https://openalex.org/S58031724","display_name":"IEEE Transactions on Industrial Electronics","issn_l":"0278-0046","issn":["0278-0046","1557-9948"],"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 Industrial Electronics","raw_type":"journal-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/A5075042717","display_name":"Amin Ullah","orcid":"https://orcid.org/0000-0001-7538-2689"},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Amin Ullah","raw_affiliation_strings":["Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-7538-2689","affiliations":[{"raw_affiliation_string":"Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul, South Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018267985","display_name":"Khan Muhammad","orcid":"https://orcid.org/0000-0002-5302-1150"},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Khan Muhammad","raw_affiliation_strings":["Department of Software, Sejong University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-5302-1150","affiliations":[{"raw_affiliation_string":"Department of Software, Sejong University, Seoul, South Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017326471","display_name":"Javier Del Ser","orcid":"https://orcid.org/0000-0002-1260-9775"},"institutions":[{"id":"https://openalex.org/I4210091170","display_name":"Euskadiko Parke Teknologikoa","ror":"https://ror.org/00caq9197","country_code":"ES","type":"archive","lineage":["https://openalex.org/I4210091170"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Javier Del Ser","raw_affiliation_strings":["TECNALIA, Derio, Bizkaia, Spain"],"raw_orcid":"https://orcid.org/0000-0002-1260-9775","affiliations":[{"raw_affiliation_string":"TECNALIA, Derio, Bizkaia, Spain","institution_ids":["https://openalex.org/I4210091170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072407810","display_name":"Sung Wook Baik","orcid":"https://orcid.org/0000-0002-6678-7788"},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sung Wook Baik","raw_affiliation_strings":["Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-6678-7788","affiliations":[{"raw_affiliation_string":"Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul, South Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045093520","display_name":"Victor Hugo C. de Albuquerque","orcid":"https://orcid.org/0000-0003-3886-4309"},"institutions":[{"id":"https://openalex.org/I3125581668","display_name":"Universidade de Fortaleza","ror":"https://ror.org/02ynbzc81","country_code":"BR","type":"education","lineage":["https://openalex.org/I3125581668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Victor Hugo C. de Albuquerque","raw_affiliation_strings":["Laboratory of Bioinformatics, University of Fortaleza, Fortaleza, Brazil"],"raw_orcid":"https://orcid.org/0000-0003-3886-4309","affiliations":[{"raw_affiliation_string":"Laboratory of Bioinformatics, University of Fortaleza, Fortaleza, Brazil","institution_ids":["https://openalex.org/I3125581668"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.3235,"has_fulltext":false,"cited_by_count":187,"citation_normalized_percentile":{"value":0.98227349,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"66","issue":"12","first_page":"9692","last_page":"9702"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9994000196456909,"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":0.9994000196456909,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9987999796867371,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8024349212646484},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.769584059715271},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.7521901726722717},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.747004508972168},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7266845703125},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.6894581317901611},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5399996638298035},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5044931173324585},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47661224007606506},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4339233636856079},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.41481897234916687},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3644072711467743},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35254520177841187},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19279715418815613}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8024349212646484},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.769584059715271},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.7521901726722717},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.747004508972168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7266845703125},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.6894581317901611},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5399996638298035},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5044931173324585},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47661224007606506},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4339233636856079},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.41481897234916687},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3644072711467743},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35254520177841187},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19279715418815613},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tie.2018.2881943","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tie.2018.2881943","pdf_url":null,"source":{"id":"https://openalex.org/S58031724","display_name":"IEEE Transactions on Industrial Electronics","issn_l":"0278-0046","issn":["0278-0046","1557-9948"],"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 Industrial Electronics","raw_type":"journal-article"},{"id":"pmh:oai:dsp.tecnalia.com:11556/2912","is_oa":false,"landing_page_url":"https://hdl.handle.net/11556/2912","pdf_url":null,"source":{"id":"https://openalex.org/S4306402037","display_name":"TECNALIA Publications (Fundaci\u00f3n TECNALIA Research & Innovation)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210113430","host_organization_name":"Tecnalia","host_organization_lineage":["https://openalex.org/I4210113430"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"journal article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.550000011920929}],"awards":[{"id":"https://openalex.org/G8082801164","display_name":null,"funder_award_id":"2016R1A2B4011712","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W906515803","https://openalex.org/W1750953599","https://openalex.org/W1899185266","https://openalex.org/W1920196880","https://openalex.org/W1923404803","https://openalex.org/W1955904464","https://openalex.org/W1960777822","https://openalex.org/W1973873770","https://openalex.org/W1981781955","https://openalex.org/W1984551518","https://openalex.org/W2064776690","https://openalex.org/W2095035335","https://openalex.org/W2100916003","https://openalex.org/W2105101328","https://openalex.org/W2126579184","https://openalex.org/W2143267104","https://openalex.org/W2161969291","https://openalex.org/W2163292664","https://openalex.org/W2172806452","https://openalex.org/W2174646748","https://openalex.org/W2194775991","https://openalex.org/W2239827863","https://openalex.org/W2259801182","https://openalex.org/W2294420641","https://openalex.org/W2295107390","https://openalex.org/W2314087435","https://openalex.org/W2322020277","https://openalex.org/W2332833656","https://openalex.org/W2410347997","https://openalex.org/W2462496837","https://openalex.org/W2471775118","https://openalex.org/W2522347965","https://openalex.org/W2558965306","https://openalex.org/W2560474170","https://openalex.org/W2599627952","https://openalex.org/W2746910024","https://openalex.org/W2766819565","https://openalex.org/W2767334695","https://openalex.org/W2767514117","https://openalex.org/W2767690801","https://openalex.org/W2769581371","https://openalex.org/W2780222614","https://openalex.org/W2789621530","https://openalex.org/W2791512297","https://openalex.org/W2807862495","https://openalex.org/W2885060605","https://openalex.org/W2886810500","https://openalex.org/W2891226593","https://openalex.org/W2951229220","https://openalex.org/W2963163009","https://openalex.org/W2963996492","https://openalex.org/W2964191259","https://openalex.org/W2964298947","https://openalex.org/W4294557331","https://openalex.org/W6600983433","https://openalex.org/W6639927594","https://openalex.org/W6640153615","https://openalex.org/W6684983439","https://openalex.org/W6689880502","https://openalex.org/W6749781174"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175","https://openalex.org/W4230315250"],"abstract_inverted_index":{"Nowadays":[0],"digital":[1],"surveillance":[2,27,62,70],"systems":[3],"are":[4,81,105,143],"universally":[5],"installed":[6],"for":[7,18,58,125,135,163],"continuously":[8],"collecting":[9],"enormous":[10],"amounts":[11],"of":[12,21,31,97,103,112,159],"data,":[13],"thereby":[14],"requiring":[15],"human":[16,91],"monitoring":[17],"the":[19,29,84,101,109,130,154,157,160],"identification":[20],"different":[22,146],"activities":[23,39],"and":[24,37,47,149,153],"events.":[25],"Smarter":[26],"is":[28,73,123],"need":[30],"this":[32,52],"era":[33],"through":[34],"which":[35],"normal":[36],"abnormal":[38],"can":[40],"be":[41],"automatically":[42],"identified":[43],"using":[44,83,145],"artificial":[45],"intelligence":[46],"computer":[48],"vision":[49],"technology.":[50],"In":[51],"paper,":[53],"we":[54],"propose":[55],"a":[56,113,118],"framework":[57],"activity":[59,99,136,150,164],"recognition":[60,151,165],"in":[61,100,129,166],"videos":[63],"captured":[64],"over":[65],"industrial":[66,167],"systems.":[67],"The":[68],"continuous":[69],"video":[71],"stream":[72],"first":[74],"divided":[75],"into":[76],"important":[77],"shots,":[78],"where":[79],"shots":[80],"selected":[82],"proposed":[85,161],"convolutional":[86,110],"neural":[87],"network":[88],"(CNN)":[89],"based":[90],"saliency":[92],"features.":[93],"Next,":[94],"temporal":[95,131],"features":[96,134],"an":[98],"sequence":[102],"frames":[104],"extracted":[106],"by":[107],"utilizing":[108],"layers":[111],"FlowNet2":[114],"CNN":[115],"model.":[116],"Finally,":[117],"multilayer":[119],"long":[120],"short-term":[121],"memory":[122],"presented":[124],"learning":[126],"long-term":[127],"sequences":[128],"optical":[132],"flow":[133],"recognition.":[137],"Experiments":[138],"<sup":[139],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[140],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">11</sup>":[141],"https://github.com/Aminullah6264/Activity_Rec_ML-LSTM.":[142],"conducted":[144],"benchmark":[147],"action":[148],"datasets,":[152],"results":[155],"reveal":[156],"effectiveness":[158],"method":[162],"settings":[168],"compared":[169],"with":[170],"state-of-the-art":[171],"methods.":[172]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":40},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":27},{"year":2020,"cited_by_count":38},{"year":2019,"cited_by_count":15}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
