{"id":"https://openalex.org/W4210905632","doi":"https://doi.org/10.1145/3492323.3495571","title":"A short survey on deep learning for skeleton-based action recognition","display_name":"A short survey on deep learning for skeleton-based action recognition","publication_year":2021,"publication_date":"2021-12-06","ids":{"openalex":"https://openalex.org/W4210905632","doi":"https://doi.org/10.1145/3492323.3495571"},"language":"en","primary_location":{"id":"doi:10.1145/3492323.3495571","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3492323.3495571","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion","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/A5100391934","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-3143-0623"},"institutions":[{"id":"https://openalex.org/I153648349","display_name":"University of Leicester","ror":"https://ror.org/04h699437","country_code":"GB","type":"education","lineage":["https://openalex.org/I153648349"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["University of Leicester, Leicester, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Leicester, Leicester, UK","institution_ids":["https://openalex.org/I153648349"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100434437","display_name":"Yudong Zhang","orcid":"https://orcid.org/0000-0002-4870-1493"},"institutions":[{"id":"https://openalex.org/I153648349","display_name":"University of Leicester","ror":"https://ror.org/04h699437","country_code":"GB","type":"education","lineage":["https://openalex.org/I153648349"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yu-Dong Zhang","raw_affiliation_strings":["University of Leicester, Leicester, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Leicester, Leicester, UK","institution_ids":["https://openalex.org/I153648349"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2911,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.58199061,"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":"1","last_page":"6"},"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9965000152587891,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9884999990463257,"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/computer-science","display_name":"Computer science","score":0.7566567063331604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7056172490119934},{"id":"https://openalex.org/keywords/skeleton","display_name":"Skeleton (computer programming)","score":0.704339325428009},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6646822690963745},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.639374852180481},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.6273119449615479},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5129584670066833},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49369415640830994},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.46789753437042236},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40477970242500305},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40087074041366577},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39498400688171387},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0790884792804718},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.07191109657287598}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7566567063331604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7056172490119934},{"id":"https://openalex.org/C18969341","wikidata":"https://www.wikidata.org/wiki/Q1169129","display_name":"Skeleton (computer programming)","level":2,"score":0.704339325428009},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6646822690963745},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.639374852180481},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.6273119449615479},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5129584670066833},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49369415640830994},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.46789753437042236},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40477970242500305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40087074041366577},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39498400688171387},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0790884792804718},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.07191109657287598},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3492323.3495571","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3492323.3495571","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion","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":38,"referenced_works":["https://openalex.org/W28988658","https://openalex.org/W1950788856","https://openalex.org/W2000059041","https://openalex.org/W2086509056","https://openalex.org/W2093655440","https://openalex.org/W2098190380","https://openalex.org/W2100774779","https://openalex.org/W2144380653","https://openalex.org/W2244851982","https://openalex.org/W2415469094","https://openalex.org/W2607707631","https://openalex.org/W2739179646","https://openalex.org/W2751841288","https://openalex.org/W2778523960","https://openalex.org/W2797382244","https://openalex.org/W2798644314","https://openalex.org/W2883585959","https://openalex.org/W2903831537","https://openalex.org/W2948058585","https://openalex.org/W2962730651","https://openalex.org/W2963076818","https://openalex.org/W2963177663","https://openalex.org/W2964134613","https://openalex.org/W2973492437","https://openalex.org/W2981341885","https://openalex.org/W3043878998","https://openalex.org/W3096803658","https://openalex.org/W3119171349","https://openalex.org/W3119523940","https://openalex.org/W3123784868","https://openalex.org/W3138674438","https://openalex.org/W4230005465","https://openalex.org/W4234552385","https://openalex.org/W4240226474","https://openalex.org/W6639383027","https://openalex.org/W6658929250","https://openalex.org/W6722979031","https://openalex.org/W6747071875"],"related_works":["https://openalex.org/W2953562271","https://openalex.org/W2334655667","https://openalex.org/W4375867731","https://openalex.org/W2464530384","https://openalex.org/W2105938841","https://openalex.org/W4253358700","https://openalex.org/W2004108207","https://openalex.org/W4399611050","https://openalex.org/W2999049608","https://openalex.org/W3170431411"],"abstract_inverted_index":{"Motion":[0],"recognition":[1,31,87,119],"is":[2],"an":[3],"essential":[4],"aspect":[5],"of":[6,14,23,46,62,101,110,132,141,147,158,161],"computer":[7],"vision":[8],"used":[9],"in":[10,144],"a":[11,114],"wide":[12],"range":[13],"fields":[15],"and":[16,39,44,65,71,89,135,152],"has":[17,74],"received":[18],"much":[19],"attention":[20],"as":[21],"one":[22],"the":[24,42,52,58,97,108,130,142,145,155],"most":[25,59],"popular":[26],"research":[27,73,143],"topics.":[28],"Traditional":[29],"motion":[30],"studies":[32],"are":[33,57],"mainly":[34],"based":[35,120],"on":[36,121],"RGB":[37,47],"images":[38],"videos,":[40],"but":[41],"lighting":[43],"viewpoint":[45],"data":[48,64,134],"can":[49],"easily":[50],"affect":[51],"model":[53],"performance.":[54],"Skeleton":[55],"sequences":[56,80],"common":[60,137],"type":[61],"coordinate":[63],"avoid":[66],"these":[67],"problems.":[68],"Therefore,":[69],"more":[70,72],"been":[75,93],"conducted":[76],"to":[77,84],"combine":[78],"skeleton":[79],"with":[81,107],"deep":[82],"learning":[83],"solve":[85],"action":[86,118,150],"problems,":[88],"awe-inspiring":[90],"results":[91],"have":[92],"obtained.":[94],"In":[95,124],"particular,":[96],"recent":[98],"rapid":[99],"emergence":[100],"GCN":[102],"methods,":[103],"which":[104],"fit":[105],"well":[106],"characteristics":[109],"skeletal":[111,122,133,148],"data,":[112],"offers":[113],"promising":[115],"future":[116,156],"for":[117],"sequences.":[123],"this":[125,159],"paper,":[126],"we":[127],"first":[128],"introduce":[129],"acquisition":[131],"some":[136,140],"datasets,":[138],"summarise":[139],"field":[146],"sequence-based":[149],"recognition,":[151],"briefly":[153],"discuss":[154],"directions":[157],"kind":[160],"research.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
