{"id":"https://openalex.org/W2909099656","doi":"https://doi.org/10.1109/siprocess.2018.8600485","title":"Towards 3D Human Action Recognition Using a Distilled CNN Model","display_name":"Towards 3D Human Action Recognition Using a Distilled CNN Model","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2909099656","doi":"https://doi.org/10.1109/siprocess.2018.8600485","mag":"2909099656"},"language":"en","primary_location":{"id":"doi:10.1109/siprocess.2018.8600485","doi":"https://doi.org/10.1109/siprocess.2018.8600485","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siprocess.2018.8600485","pdf_url":null,"source":{"id":"https://openalex.org/S4363607935","display_name":"IEEE International Conference on Signal and Image Processing (ICSIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5102528339","display_name":"Jingjing Ren","orcid":"https://orcid.org/0009-0005-7777-1684","observed_orcids":["https://orcid.org/0009-0005-7777-1684"]},"institutions":[{"id":"https://openalex.org/I51158804","display_name":"Massey University","ror":"https://ror.org/052czxv31","country_code":"NZ","type":"education","lineage":["https://openalex.org/I51158804"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"J. Ren","raw_affiliation_strings":["Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand","institution_ids":["https://openalex.org/I51158804"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088177066","display_name":"Napoleon H. Reyes","orcid":"https://orcid.org/0000-0002-0683-436X","observed_orcids":["https://orcid.org/0000-0002-0683-436X"]},"institutions":[{"id":"https://openalex.org/I51158804","display_name":"Massey University","ror":"https://ror.org/052czxv31","country_code":"NZ","type":"education","lineage":["https://openalex.org/I51158804"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"N. H. Reyes","raw_affiliation_strings":["Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand","institution_ids":["https://openalex.org/I51158804"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018208301","display_name":"Andre L. C. Barczak","orcid":"https://orcid.org/0000-0001-7648-285X","observed_orcids":["https://orcid.org/0000-0001-7648-285X"]},"institutions":[{"id":"https://openalex.org/I51158804","display_name":"Massey University","ror":"https://ror.org/052czxv31","country_code":"NZ","type":"education","lineage":["https://openalex.org/I51158804"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"A. L.C. Barczak","raw_affiliation_strings":["Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand","institution_ids":["https://openalex.org/I51158804"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060460869","display_name":"Chris J. Scogings","orcid":"https://orcid.org/0000-0001-7452-5481","observed_orcids":["https://orcid.org/0000-0001-7452-5481"]},"institutions":[{"id":"https://openalex.org/I51158804","display_name":"Massey University","ror":"https://ror.org/052czxv31","country_code":"NZ","type":"education","lineage":["https://openalex.org/I51158804"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"C. Scogings","raw_affiliation_strings":["Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand","institution_ids":["https://openalex.org/I51158804"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100674094","display_name":"Mingzhe Liu","orcid":"https://orcid.org/0000-0001-7054-997X","observed_orcids":["https://orcid.org/0000-0001-7054-997X"]},"institutions":[{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"M. Liu","raw_affiliation_strings":["SKLGP, Chengdu University of Technology, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SKLGP, Chengdu University of Technology, Chengdu, China","institution_ids":["https://openalex.org/I31595395"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1468,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.46007203,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":95},"biblio":{"volume":"15","issue":null,"first_page":"7","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9990000128746033,"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":0.9990000128746033,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.000699999975040555,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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":9.999999747378752e-05,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/3d-human-action-recognition","display_name":"3D human action recognition","score":0.9890000224113464},{"id":"https://openalex.org/keywords/skeleton-sequences","display_name":"skeleton sequences","score":0.8550000190734863},{"id":"https://openalex.org/keywords/spatiotemporal-feature-extraction","display_name":"spatiotemporal feature extraction","score":0.7440000176429749},{"id":"https://openalex.org/keywords/knowledge-distillation","display_name":"knowledge distillation","score":0.921999990940094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7820298075675964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7456200122833252},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6497806906700134},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6461196541786194},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5997871160507202},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5995224714279175},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.5865168571472168},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.5658029913902283},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.5321169495582581},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.48274630308151245},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.45271432399749756},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4140596389770508},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3670327663421631},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.13557249307632446},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1285904049873352},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/siprocess.2018.8600485","doi":"https://doi.org/10.1109/siprocess.2018.8600485","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siprocess.2018.8600485","pdf_url":null,"source":{"id":"https://openalex.org/S4363607935","display_name":"IEEE International Conference on Signal and Image Processing (ICSIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1563923697","https://openalex.org/W1923186697","https://openalex.org/W1979378996","https://openalex.org/W1995562189","https://openalex.org/W2007057255","https://openalex.org/W2048821851","https://openalex.org/W2060280062","https://openalex.org/W2086509056","https://openalex.org/W2124688298","https://openalex.org/W2142203883","https://openalex.org/W2143267104","https://openalex.org/W2145546283","https://openalex.org/W2156387975","https://openalex.org/W2172156083","https://openalex.org/W2244851982","https://openalex.org/W2345474290","https://openalex.org/W2415469094","https://openalex.org/W2442651457","https://openalex.org/W2563154851","https://openalex.org/W2593146028","https://openalex.org/W2612707971","https://openalex.org/W2619315488","https://openalex.org/W2778523960","https://openalex.org/W2962949934","https://openalex.org/W2964134613"],"related_works":["https://openalex.org/W2886450608","https://openalex.org/W3135617766","https://openalex.org/W2751139225","https://openalex.org/W2794805739","https://openalex.org/W3012058546","https://openalex.org/W2887446291","https://openalex.org/W2897989494","https://openalex.org/W2554408731","https://openalex.org/W2567416773","https://openalex.org/W7047090341"],"abstract_inverted_index":{"Motivated":[0],"by":[1],"the":[2,24,44,87,100,103,111,114,117,122],"remarkable":[3],"performance":[4],"achieved":[5],"using":[6],"deep":[7],"learning":[8],"strategies":[9],"in":[10,108],"solving":[11],"action":[12,94,129],"recognition":[13,130],"tasks,":[14],"an":[15],"effective,":[16],"yet":[17],"simple":[18],"method":[19,105],"is":[20,82,106],"proposed":[21,104],"for":[22,92],"encoding":[23],"spatiotemporal":[25],"information":[26,67],"of":[27,57,90,102,110,113],"skeleton":[28,58],"sequences":[29],"into":[30],"color":[31,61],"texture":[32],"images,":[33],"referred":[34],"to":[35,63,85],"as":[36,60,133],"Skeletal":[37],"Optical":[38],"Flows":[39],"(SOFs).":[40],"SOFs":[41,91],"collectively":[42],"represent":[43],"kinetic":[45],"energy,":[46],"predefined":[47],"angles":[48],"and":[49,68,121,136],"pair-wise":[50],"displacements":[51],"between":[52],"joints":[53],"over":[54],"consecutive":[55],"frames":[56],"data,":[59],"variations":[62],"capture":[64],"meaningful":[65],"temporal":[66],"make":[69],"them":[70],"highly":[71],"interpretable.":[72],"A":[73],"novel":[74],"Convolutional":[75],"Neural":[76],"Network":[77],"with":[78],"Correctness-Vigilant":[79],"Regularizer":[80],"(CVR-CNN)":[81],"then":[83],"employed":[84],"exploit":[86],"discriminative":[88],"features":[89],"human":[93],"recognition.":[95],"Empirical":[96],"results":[97],"show":[98],"that":[99],"efficiency":[101],"superior":[107],"terms":[109],"generalizability":[112],"generated":[115],"model,":[116],"training":[118],"convergence":[119],"speed,":[120],"resulting":[123],"classification":[124],"accuracy":[125],"on":[126],"commonly":[127],"used":[128],"datasets,":[131],"such":[132],"MHAD,":[134],"HDM05":[135],"NTU":[137],"RGB+D.":[138]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2019-01-25T00:00:00"}