{"id":"https://openalex.org/W4391306894","doi":"https://doi.org/10.1109/smc53992.2023.10393912","title":"Human Action Recognition Using Multi-Stream Fusion and Hybrid Deep Neural Networks","display_name":"Human Action Recognition Using Multi-Stream Fusion and Hybrid Deep Neural Networks","publication_year":2023,"publication_date":"2023-10-01","ids":{"openalex":"https://openalex.org/W4391306894","doi":"https://doi.org/10.1109/smc53992.2023.10393912"},"language":"en","primary_location":{"id":"doi:10.1109/smc53992.2023.10393912","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc53992.2023.10393912","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5022765230","display_name":"Saurabh Chopra","orcid":null},"institutions":[{"id":"https://openalex.org/I184558857","display_name":"Royal Holloway University of London","ror":"https://ror.org/04g2vpn86","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I184558857"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Saurabh Chopra","raw_affiliation_strings":["University of London,Department of Computer Science Royal Holloway,Surrey,United Kingdom","Department of Computer Science Royal Holloway, University of London, Surrey, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of London,Department of Computer Science Royal Holloway,Surrey,United Kingdom","institution_ids":["https://openalex.org/I184558857"]},{"raw_affiliation_string":"Department of Computer Science Royal Holloway, University of London, Surrey, United Kingdom","institution_ids":["https://openalex.org/I184558857"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418950","display_name":"Li Zhang","orcid":"https://orcid.org/0000-0001-6674-692X"},"institutions":[{"id":"https://openalex.org/I184558857","display_name":"Royal Holloway University of London","ror":"https://ror.org/04g2vpn86","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I184558857"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["University of London,Department of Computer Science Royal Holloway,Surrey,United Kingdom","Department of Computer Science Royal Holloway, University of London, Surrey, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of London,Department of Computer Science Royal Holloway,Surrey,United Kingdom","institution_ids":["https://openalex.org/I184558857"]},{"raw_affiliation_string":"Department of Computer Science Royal Holloway, University of London, Surrey, United Kingdom","institution_ids":["https://openalex.org/I184558857"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018896387","display_name":"Ming Jiang","orcid":"https://orcid.org/0000-0001-6439-5476"},"institutions":[{"id":"https://openalex.org/I5728261","display_name":"University of Sunderland","ror":"https://ror.org/04p55hr04","country_code":"GB","type":"education","lineage":["https://openalex.org/I5728261"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ming Jiang","raw_affiliation_strings":["Faculty of Technology,Department of Computer Science,Sunderland,United Kingdom","Department of Computer Science, Faculty of Technology, Sunderland, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Technology,Department of Computer Science,Sunderland,United Kingdom","institution_ids":["https://openalex.org/I5728261"]},{"raw_affiliation_string":"Department of Computer Science, Faculty of Technology, Sunderland, United Kingdom","institution_ids":["https://openalex.org/I5728261"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7859,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.75168406,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4852","last_page":"4858"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9998999834060669,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9998999834060669,"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":0.9941999912261963,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9923999905586243,"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":0.8325947523117065},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7614070177078247},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.726033091545105},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.7248463034629822},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5657636523246765},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.5555802583694458},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5169524550437927},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4371846914291382},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.41401544213294983},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32784920930862427},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3026798665523529}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8325947523117065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7614070177078247},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.726033091545105},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.7248463034629822},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5657636523246765},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.5555802583694458},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5169524550437927},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4371846914291382},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.41401544213294983},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32784920930862427},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3026798665523529},{"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/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc53992.2023.10393912","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc53992.2023.10393912","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W2156303437","https://openalex.org/W2735305611","https://openalex.org/W2750690203","https://openalex.org/W2770446450","https://openalex.org/W2886479397","https://openalex.org/W2924986595","https://openalex.org/W2948048211","https://openalex.org/W2955935028","https://openalex.org/W2963155035","https://openalex.org/W2963166524","https://openalex.org/W2978506973","https://openalex.org/W2988630963","https://openalex.org/W3015483809","https://openalex.org/W3034257141","https://openalex.org/W3083667980","https://openalex.org/W3113067059","https://openalex.org/W3134090706","https://openalex.org/W3214977267","https://openalex.org/W4200363638","https://openalex.org/W4210655669","https://openalex.org/W4226153949","https://openalex.org/W4313046672","https://openalex.org/W4379467046","https://openalex.org/W4380881652"],"related_works":["https://openalex.org/W3107204728","https://openalex.org/W4287591324","https://openalex.org/W3108503355","https://openalex.org/W4226420367","https://openalex.org/W2962876041","https://openalex.org/W3090555870","https://openalex.org/W3022820045","https://openalex.org/W2801655600","https://openalex.org/W2899027234","https://openalex.org/W2796878614"],"abstract_inverted_index":{"Action":[0],"Recognition":[1],"in":[2,9,25],"videos":[3],"is":[4,124,149],"a":[5,159],"topic":[6],"of":[7,12,80,112,155,164,200,203],"interest":[8],"the":[10,100,146,197,213],"area":[11],"computer":[13],"vision,":[14],"due":[15],"to":[16,126],"potential":[17],"applications":[18],"such":[19,59,82],"as":[20,60,69,83,99,136,138],"multimedia":[21],"indexing":[22],"and":[23,35,48,94,114,129,175,187,209],"surveillance":[24],"public":[26],"areas.":[27],"In":[28,76],"this":[29],"research,":[30],"we":[31],"first":[32],"propose":[33],"spatial":[34,128,171],"temporal":[36,130],"Convolutional":[37],"Neural":[38,62],"Network":[39,63],"(CNNs),":[40],"based":[41],"on":[42,134,143],"transfer":[43],"learning":[44],"using":[45,184],"ResNetl0l,":[46],"GoogleNet":[47],"VGG16,":[49],"for":[50,72,102,190,215],"undertaking":[51,216],"human":[52],"action":[53,74,103,219],"recognition.":[54,104,220],"Besides":[55],"that,":[56],"hybrid":[57],"networks":[58,111,214],"CNN-Recurrent":[61],"(RNN)":[64],"models":[65,116,157,166],"are":[66,97,117,179,182],"also":[67],"exploited":[68,98],"encoder-decoder":[70],"architectures":[71],"video":[73,218],"classification.":[75],"particular,":[77],"different":[78],"types":[79],"RNNs":[81],"Long":[84],"Short-Term":[85],"Memory":[86],"(LSTM),":[87],"Bidirectional-LSTM":[88],"(BiLSTM),":[89],"Gated":[90],"Recurrent":[91],"Unit":[92],"(GRU),":[93],"Bidirectional-GRU":[95],"(BiGRU),":[96],"decoders":[101],"To":[105],"further":[106],"enhance":[107],"performance,":[108],"diverse":[109],"aggregation":[110],"CNN":[113,131,139],"CNN-RNN":[115],"implemented.":[118],"Specifically,":[119],"an":[120],"Average":[121,201],"Fusion":[122,202],"method":[123],"used":[125],"integrate":[127],"s":[132],"trained":[133,142],"images,":[135],"well":[137],"-":[140],"RNN":[141],"videos,":[144],"where":[145],"final":[147],"classification":[148],"formed":[150],"by":[151],"combining":[152],"Softmax":[153],"scores":[154],"these":[156],"via":[158],"late":[160],"fusion.":[161],"A":[162],"total":[163],"22":[165],"(1":[167],"motion":[168],"CNN,":[169],"3":[170],"CNNs,":[172],"12":[173],"CNN-RNNs":[174],"6":[176],"fusion":[177],"networks)":[178],"implemented":[180],"which":[181],"evaluated":[183],"UCF11,":[185],"UCFSO,":[186],"UCF10l":[188],"datasets":[189],"performance":[191],"comparison.":[192],"The":[193],"empirical":[194],"results":[195],"indicate":[196],"significant":[198],"efficiency":[199],"multiple":[204],"Spatial-CNNs":[205],"with":[206],"one":[207],"Motion-CNN,":[208],"ResNet101-BiGRU,":[210],"among":[211],"all":[212],"realistic":[217]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
