{"id":"https://openalex.org/W4308234223","doi":"https://doi.org/10.1109/icip46576.2022.9897258","title":"An Efficient Framework for Human Action Recognition Based on Graph Convolutional Networks","display_name":"An Efficient Framework for Human Action Recognition Based on Graph Convolutional Networks","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308234223","doi":"https://doi.org/10.1109/icip46576.2022.9897258"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897258","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897258","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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 Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.1109/ICIP46576.2022.9897258","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073644146","display_name":"Nikolaos Kilis","orcid":null},"institutions":[{"id":"https://openalex.org/I21370196","display_name":"Aristotle University of Thessaloniki","ror":"https://ror.org/02j61yw88","country_code":"GR","type":"education","lineage":["https://openalex.org/I21370196"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Nikolaos Kilis","raw_affiliation_strings":["Aristotle University of Thessaloniki,Department of Informatics,Greece","Department of Informatics, Aristotle University of Thessaloniki, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aristotle University of Thessaloniki,Department of Informatics,Greece","institution_ids":["https://openalex.org/I21370196"]},{"raw_affiliation_string":"Department of Informatics, Aristotle University of Thessaloniki, Greece","institution_ids":["https://openalex.org/I21370196"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024066107","display_name":"Christos Papaioannidis","orcid":"https://orcid.org/0000-0002-3839-4514"},"institutions":[{"id":"https://openalex.org/I21370196","display_name":"Aristotle University of Thessaloniki","ror":"https://ror.org/02j61yw88","country_code":"GR","type":"education","lineage":["https://openalex.org/I21370196"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Christos Papaioannidis","raw_affiliation_strings":["Aristotle University of Thessaloniki,Department of Informatics,Greece","Department of Informatics, Aristotle University of Thessaloniki, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aristotle University of Thessaloniki,Department of Informatics,Greece","institution_ids":["https://openalex.org/I21370196"]},{"raw_affiliation_string":"Department of Informatics, Aristotle University of Thessaloniki, Greece","institution_ids":["https://openalex.org/I21370196"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070716976","display_name":"Ioannis Mademlis","orcid":"https://orcid.org/0000-0001-5479-0632"},"institutions":[{"id":"https://openalex.org/I21370196","display_name":"Aristotle University of Thessaloniki","ror":"https://ror.org/02j61yw88","country_code":"GR","type":"education","lineage":["https://openalex.org/I21370196"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Ioannis Mademlis","raw_affiliation_strings":["Aristotle University of Thessaloniki,Department of Informatics,Greece","Department of Informatics, Aristotle University of Thessaloniki, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aristotle University of Thessaloniki,Department of Informatics,Greece","institution_ids":["https://openalex.org/I21370196"]},{"raw_affiliation_string":"Department of Informatics, Aristotle University of Thessaloniki, Greece","institution_ids":["https://openalex.org/I21370196"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061130224","display_name":"Ioannis Pitas","orcid":"https://orcid.org/0009-0006-7555-8641"},"institutions":[{"id":"https://openalex.org/I21370196","display_name":"Aristotle University of Thessaloniki","ror":"https://ror.org/02j61yw88","country_code":"GR","type":"education","lineage":["https://openalex.org/I21370196"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Ioannis Pitas","raw_affiliation_strings":["Aristotle University of Thessaloniki,Department of Informatics,Greece","Department of Informatics, Aristotle University of Thessaloniki, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aristotle University of Thessaloniki,Department of Informatics,Greece","institution_ids":["https://openalex.org/I21370196"]},{"raw_affiliation_string":"Department of Informatics, Aristotle University of Thessaloniki, Greece","institution_ids":["https://openalex.org/I21370196"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21370196"],"apc_list":null,"apc_paid":null,"fwci":0.3539,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.67501774,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1441","last_page":"1445"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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":1.0,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9965000152587891,"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":0.9872000217437744,"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/adjacency-matrix","display_name":"Adjacency matrix","score":0.7875974178314209},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.784986674785614},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.5608834028244019},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.5581266283988953},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.528003454208374},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5194275379180908},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.506597638130188},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3540460467338562},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23886269330978394},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.219181090593338}],"concepts":[{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.7875974178314209},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.784986674785614},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.5608834028244019},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.5581266283988953},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.528003454208374},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5194275379180908},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.506597638130188},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3540460467338562},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23886269330978394},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.219181090593338},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icip46576.2022.9897258","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897258","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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 Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:zenodo.org:10379363","is_oa":true,"landing_page_url":"https://doi.org/10.1109/ICIP46576.2022.9897258","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ICIP, IEEE International Conference on Image Processing, Bordeaux, France, 16-19 October 2022","raw_type":"info:eu-repo/semantics/conferencePaper"}],"best_oa_location":{"id":"pmh:oai:zenodo.org:10379363","is_oa":true,"landing_page_url":"https://doi.org/10.1109/ICIP46576.2022.9897258","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ICIP, IEEE International Conference on Image Processing, Bordeaux, France, 16-19 October 2022","raw_type":"info:eu-repo/semantics/conferencePaper"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1919216911","https://openalex.org/W1926645898","https://openalex.org/W1947481528","https://openalex.org/W1950788856","https://openalex.org/W2016553979","https://openalex.org/W2034014085","https://openalex.org/W2048821851","https://openalex.org/W2079447555","https://openalex.org/W2131681506","https://openalex.org/W2144380653","https://openalex.org/W2162033023","https://openalex.org/W2181300449","https://openalex.org/W2593146028","https://openalex.org/W2603861860","https://openalex.org/W2607226019","https://openalex.org/W2612706635","https://openalex.org/W2613570903","https://openalex.org/W2940457086","https://openalex.org/W2948246283","https://openalex.org/W2962730651","https://openalex.org/W2963076818","https://openalex.org/W2963465695","https://openalex.org/W2964015378","https://openalex.org/W2999237480","https://openalex.org/W3034213511","https://openalex.org/W3099768174","https://openalex.org/W4205438624","https://openalex.org/W4206982422","https://openalex.org/W6631190155","https://openalex.org/W6726873649"],"related_works":["https://openalex.org/W4213150077","https://openalex.org/W2369410163","https://openalex.org/W2059018062","https://openalex.org/W2604585036","https://openalex.org/W2078477160","https://openalex.org/W1989103179","https://openalex.org/W1991172810","https://openalex.org/W125803343","https://openalex.org/W2117632582","https://openalex.org/W2153421018"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,33,38,45,169],"novel":[4,39,78],"framework":[5,20],"for":[6,95,159],"skeleton-based":[7,163],"Human":[8],"Action":[9],"Recognition":[10],"(HAR)":[11],"based":[12,73],"on":[13,74,139],"Graph":[14],"Convolution":[15],"Networks":[16],"(GCNs).":[17],"The":[18,51,77],"proposed":[19,135],"aims":[21],"to":[22,57,69,86,130,150],"increase":[23],"human":[24,47,145,164],"action":[25,48,146,165],"recognition":[26,49],"performance":[27],"of":[28,103],"GCN-based":[29,127],"methods":[30,157],"by":[31],"incorporating":[32],"missing-joint-handling":[34,52],"pre-processing":[35,53,110],"step":[36,54,111],"and":[37,112,154,161],"adjacency":[40,79,91,114],"matrix":[41,80,92],"construction":[42,81,115],"method":[43,82,116,129],"in":[44,61,100,133],"single":[46],"pipeline.":[50],"is":[55,83,98],"utilized":[56,99,119],"infer":[58],"missing":[59],"data":[60],"the":[62,104,109,113,134,151],"input":[63],"sequence,":[64],"which":[65,97],"may":[66],"occur":[67],"due":[68],"imperfect":[70],"skeleton":[71],"extraction,":[72],"imputation":[75],"methods.":[76],"executed":[84],"offline":[85],"compute":[87],"an":[88],"improved":[89],"weighted":[90],"specifically":[93],"designed":[94],"HAR,":[96],"every":[101],"layer":[102],"employed":[105,132,152],"GCN.":[106],"Moreover,":[107],"both":[108,158],"can":[117],"be":[118,131],"along":[120],"with":[121,172],"any":[122,126],"GCN":[123,170],"architecture,":[124],"allowing":[125],"HAR":[128],"framework.":[136],"Experimental":[137],"evaluation":[138],"two":[140],"public":[141],"datasets":[142],"indicate":[143],"favorable":[144],"classification":[147],"scores":[148],"compared":[149],"baseline":[153],"all":[155],"competing":[156],"2D":[160],"3D":[162],"recognition,":[166],"while":[167],"using":[168],"architecture":[171],"less":[173],"learnable":[174],"parameters.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}