{"id":"https://openalex.org/W7171721137","doi":"https://doi.org/10.1515/comp-2025-0059","title":"Basketball motion recognition method based on DBSCAN trajectory segmental clustering","display_name":"Basketball motion recognition method based on DBSCAN trajectory segmental clustering","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7171721137","doi":"https://doi.org/10.1515/comp-2025-0059"},"language":"en","primary_location":{"id":"doi:10.1515/comp-2025-0059","is_oa":true,"landing_page_url":"https://doi.org/10.1515/comp-2025-0059","pdf_url":null,"source":{"id":"https://openalex.org/S4210177004","display_name":"Open Computer Science","issn_l":"2299-1093","issn":["2299-1093"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310313990","host_organization_name":"De Gruyter","host_organization_lineage":["https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Open Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1515/comp-2025-0059","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5143931918","display_name":"Peng Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I149240348","display_name":"Jilin Normal University","ror":"https://ror.org/00xtsag93","country_code":"CN","type":"education","lineage":["https://openalex.org/I149240348"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Guo","raw_affiliation_strings":["College of Physical Education, Jilin Normal University , Siping , 136000 , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Physical Education, Jilin Normal University , Siping , 136000 , China","institution_ids":["https://openalex.org/I149240348"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5143899342","display_name":"Xinxin Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I149240348","display_name":"Jilin Normal University","ror":"https://ror.org/00xtsag93","country_code":"CN","type":"education","lineage":["https://openalex.org/I149240348"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinxin Sun","raw_affiliation_strings":["School of Foreign Languages, Jilin Normal University , Siping , 136000 , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Foreign Languages, Jilin Normal University , Siping , 136000 , China","institution_ids":["https://openalex.org/I149240348"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149240348"],"apc_list":{"value":1050,"currency":"EUR","value_usd":1187},"apc_paid":{"value":1050,"currency":"EUR","value_usd":1187},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7906362,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"16","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.2849000096321106,"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":0.2849000096321106,"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/T14413","display_name":"Advanced Technologies in Various Fields","score":0.08789999783039093,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.07190000265836716,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/cluster-analysis","display_name":"Cluster analysis","score":0.671500027179718},{"id":"https://openalex.org/keywords/basketball","display_name":"Basketball","score":0.5565000176429749},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49079999327659607},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.44290000200271606},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.4404999911785126},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.3946000039577484},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.3682999908924103},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3400999903678894}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7042999863624573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6923999786376953},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.671500027179718},{"id":"https://openalex.org/C103189561","wikidata":"https://www.wikidata.org/wiki/Q5372","display_name":"Basketball","level":2,"score":0.5565000176429749},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5544999837875366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49079999327659607},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.44290000200271606},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.4404999911785126},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3946000039577484},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3702000081539154},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.3682999908924103},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3400999903678894},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C46576248","wikidata":"https://www.wikidata.org/wiki/Q1114630","display_name":"DBSCAN","level":5,"score":0.28700000047683716},{"id":"https://openalex.org/C2777036941","wikidata":"https://www.wikidata.org/wiki/Q6917771","display_name":"Motion analysis","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1515/comp-2025-0059","is_oa":true,"landing_page_url":"https://doi.org/10.1515/comp-2025-0059","pdf_url":null,"source":{"id":"https://openalex.org/S4210177004","display_name":"Open Computer Science","issn_l":"2299-1093","issn":["2299-1093"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310313990","host_organization_name":"De Gruyter","host_organization_lineage":["https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Open Computer Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:11cbc60bde144e1ba010e4bbfdece74c","is_oa":false,"landing_page_url":"https://doaj.org/article/11cbc60bde144e1ba010e4bbfdece74c","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Open Computer Science, Vol 16, Iss 1, Pp 3427-3435 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1515/comp-2025-0059","is_oa":true,"landing_page_url":"https://doi.org/10.1515/comp-2025-0059","pdf_url":null,"source":{"id":"https://openalex.org/S4210177004","display_name":"Open Computer Science","issn_l":"2299-1093","issn":["2299-1093"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310313990","host_organization_name":"De Gruyter","host_organization_lineage":["https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Open Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.502241313457489,"id":"https://metadata.un.org/sdg/16"},{"display_name":"Reduced inequalities","score":0.4610224664211273,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W3011934803","https://openalex.org/W3202934454","https://openalex.org/W4280583662","https://openalex.org/W4313594820","https://openalex.org/W4315628817","https://openalex.org/W4321503132","https://openalex.org/W4360584281","https://openalex.org/W4360611878","https://openalex.org/W4382051180","https://openalex.org/W4382203417","https://openalex.org/W4382240124","https://openalex.org/W4383316027","https://openalex.org/W4385415454","https://openalex.org/W4386441386","https://openalex.org/W4387444037","https://openalex.org/W4387476891","https://openalex.org/W4387776593","https://openalex.org/W4388699922","https://openalex.org/W4388817571","https://openalex.org/W4389245974","https://openalex.org/W4389454275","https://openalex.org/W4390618138","https://openalex.org/W4391092301","https://openalex.org/W4391257684","https://openalex.org/W4391942013","https://openalex.org/W4393148253","https://openalex.org/W4395957322","https://openalex.org/W4401833394","https://openalex.org/W4402041507","https://openalex.org/W4402676779"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"In":[1,159,189],"basketball":[2,58,200,253,265,273],"motion":[3,59,164,180,266],"recognition,":[4],"there":[5],"are":[6,91],"problems":[7],"such":[8,27],"as":[9,28],"difficulty":[10,16],"in":[11,17,161],"obtaining":[12],"high-quality":[13],"action":[14,77,275],"data,":[15],"coping":[18],"with":[19,53,121],"poor":[20],"discrimination":[21,147],"accuracy":[22,129],"due":[23],"to":[24,46,55,74,93,248,264],"scene":[25],"factors":[26],"movement":[29],"of":[30,33,37,51,65,79,97,113,125,131,137,143,149,156,163,178,187,237,272],"athletes,":[31],"occlusion":[32],"equipment,":[34],"and":[35,81,86,100,152,213,225,243,268],"interference":[36],"spectators.":[38],"Aiming":[39],"at":[40],"these":[41],"problems,":[42],"the":[43,63,76,95,107,110,114,117,167,173,184,190,193,205,226,244,251,257,270],"study":[44,115,174],"proposes":[45],"use":[47],"density-based":[48],"spatial":[49],"clustering":[50,236],"applications":[52],"noise":[54],"construct":[56],"a":[57,68,82,122,134,146],"recognition":[60,96,227,267],"model.":[61],"On":[62],"basis":[64],"this":[66],"model,":[67],"joint":[69,168],"visual-inertial":[70],"sensor":[71,170],"is":[72],"introduced":[73],"collect":[75],"data":[78],"basketball,":[80],"hybrid":[83],"Gaussian":[84],"model":[85,112,195,245,259],"dynamic":[87],"time":[88,148,228],"warping":[89],"algorithm":[90],"added":[92],"realize":[94],"single":[98,185],"joints":[99],"multi-joints.":[101],"The":[102,215,235],"outcomes":[103],"found":[104],"that":[105],"on":[106],"performance":[108,120],"test,":[109,192],"proposed":[111,194],"had":[116,175],"best":[118],"overall":[119],"loss":[123],"rate":[124,130,136,155,218],"1.02":[126],"%,":[127,133,139,145,224],"an":[128,140,153],"97.02":[132],"recall":[135],"95.26":[138],"F1":[141],"value":[142],"95.17":[144],"1.3":[150],"s,":[151],"error":[154],"0.9":[157],"%.":[158],"addition,":[160],"terms":[162],"trajectory":[165,181],"quality,":[166],"vision-inertial":[169],"designed":[171],"by":[172],"better":[176],"quality":[177],"captured":[179],"smoothness":[182],"than":[183,222,232],"type":[186],"sensor.":[188],"example":[191],"could":[196],"well":[197,262],"recognize":[198,250],"six":[199],"movements:":[201],"dribbling":[202],"ball,":[203,206],"passing":[204],"shooting,":[207],"side":[208],"step":[209],"defense,":[210],"crossing":[211],"over,":[212],"dealing.":[214],"correct":[216],"identification":[217],"was":[219,229,246],"no":[220,230],"less":[221],"90":[223],"more":[231],"2":[233],"s.":[234],"features":[238],"also":[239],"showed":[240],"clear":[241],"clusters,":[242],"able":[247],"correctly":[249],"complex":[252],"scenarios.":[254],"Taken":[255],"together,":[256],"research":[258],"can":[260],"be":[261],"applied":[263],"promote":[269],"improvement":[271],"players\u2019":[274],"level.":[276]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-07-30T00:00:00"}
