{"id":"https://openalex.org/W4224094347","doi":"https://doi.org/10.1145/3523150.3523167","title":"Action Recognition Based on Person-Object Relationship Spatio-Temporal Graph","display_name":"Action Recognition Based on Person-Object Relationship Spatio-Temporal Graph","publication_year":2022,"publication_date":"2022-01-15","ids":{"openalex":"https://openalex.org/W4224094347","doi":"https://doi.org/10.1145/3523150.3523167"},"language":"en","primary_location":{"id":"doi:10.1145/3523150.3523167","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3523150.3523167","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 The 6th International Conference on Machine Learning and Soft Computing","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/A5101930341","display_name":"Tianxiao Wang","orcid":"https://orcid.org/0009-0004-0242-2861"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianxiao Wang","raw_affiliation_strings":["Wuhan University Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University Science and Technology, China","institution_ids":["https://openalex.org/I43922553"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100361857","display_name":"Jun Liu","orcid":"https://orcid.org/0000-0002-4365-4165"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Liu","raw_affiliation_strings":["Wuhan University Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University Science and Technology, China","institution_ids":["https://openalex.org/I43922553"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101930341"],"corresponding_institution_ids":["https://openalex.org/I43922553"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02256844,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2021","issue":null,"first_page":"105","last_page":"110"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9954000115394592,"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":0.9902999997138977,"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":0.6745472550392151},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6562709212303162},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5882434248924255},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.516446590423584},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49790406227111816},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.4734518229961395},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.4673362672328949},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42285022139549255},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.29187020659446716}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6745472550392151},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6562709212303162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5882434248924255},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.516446590423584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49790406227111816},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.4734518229961395},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.4673362672328949},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42285022139549255},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.29187020659446716},{"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3523150.3523167","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3523150.3523167","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 The 6th International Conference on Machine Learning and Soft Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/10"},{"display_name":"Peace, Justice and strong institutions","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W1981276685","https://openalex.org/W2056339039","https://openalex.org/W2098339052","https://openalex.org/W2544747291","https://openalex.org/W2560474170","https://openalex.org/W2806331055","https://openalex.org/W2962934715","https://openalex.org/W4234552385","https://openalex.org/W4240153047","https://openalex.org/W4242177601","https://openalex.org/W6601205648"],"related_works":["https://openalex.org/W2062195135","https://openalex.org/W2737719445","https://openalex.org/W1834370135","https://openalex.org/W2114275278","https://openalex.org/W1489511283","https://openalex.org/W2974914859","https://openalex.org/W2026565050","https://openalex.org/W2110244802","https://openalex.org/W949345935","https://openalex.org/W2507540959"],"abstract_inverted_index":{"Human":[0],"action":[1,19,40],"recognition":[2,20,41],"has":[3],"a":[4],"wide":[5],"range":[6],"of":[7,28,47,58,73,80,90,142],"applications":[8],"in":[9,84,101],"real":[10],"life.":[11],"Aiming":[12],"at":[13],"the":[14,17,24,29,32,35,45,56,59,66,69,71,74,77,81,85,88,91,94,98,102,105,112,139],"problem":[15],"that":[16],"existing":[18],"framework":[21],"cannot":[22],"describe":[23,65],"current":[25],"object":[26,82,99],"state":[27,79],"behavior":[30,128],"and":[31,50,87,107,120,136,138,144],"interaction":[33,95],"between":[34,97],"object,this":[36],"paper":[37],"proposes":[38],"an":[39],"method":[42,54,131],"based":[43],"on":[44,134],"modeling":[46],"person-object":[48,60,106,113],"relationship":[49,61,114],"spatio-temporal":[51,62,115],"relationship.":[52,110],"This":[53],"uses":[55],"representation":[57],"graph":[63,75,92,116,118],"to":[64,124],"behavior.":[67],"In":[68],"graph,":[70],"nodes":[72],"represent":[76,93],"time-space":[78],"participating":[83,100],"behavior,":[86,103],"edges":[89],"relationships":[96],"including":[104],"time":[108],"sequence":[109],"Optimize":[111],"through":[117],"convolution,":[119],"use":[121],"sparse":[122],"coding":[123],"convert":[125],"it":[126],"into":[127],"indicators.":[129],"The":[130],"was":[132],"tested":[133],"HMDB51":[135],"UCF101,":[137],"experimental":[140],"results":[141],"77.6%":[143],"96.5%":[145],"were":[146],"achieved":[147],"respectively.":[148]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
