{"id":"https://openalex.org/W3213364958","doi":"https://doi.org/10.1109/thms.2021.3121649","title":"Simple But Effective: Upper-Body Geometric Features for Traffic Command Gesture Recognition","display_name":"Simple But Effective: Upper-Body Geometric Features for Traffic Command Gesture Recognition","publication_year":2021,"publication_date":"2021-11-05","ids":{"openalex":"https://openalex.org/W3213364958","doi":"https://doi.org/10.1109/thms.2021.3121649","mag":"3213364958"},"language":"en","primary_location":{"id":"doi:10.1109/thms.2021.3121649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/thms.2021.3121649","pdf_url":null,"source":{"id":"https://openalex.org/S2476799526","display_name":"IEEE Transactions on Human-Machine Systems","issn_l":"2168-2291","issn":["2168-2291","2168-2305"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Human-Machine Systems","raw_type":"journal-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/A5024217022","display_name":"Sijia Wang","orcid":"https://orcid.org/0000-0001-5075-1900"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sijia Wang","raw_affiliation_strings":["School of Vehicle and Mobility, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5075-1900","affiliations":[{"raw_affiliation_string":"School of Vehicle and Mobility, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kun Jiang","orcid":"https://orcid.org/0000-0001-7282-172X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Jiang","raw_affiliation_strings":["School of Vehicle and Mobility, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7282-172X","affiliations":[{"raw_affiliation_string":"School of Vehicle and Mobility, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033905932","display_name":"Junjie Chen","orcid":"https://orcid.org/0000-0002-3536-3501"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjie Chen","raw_affiliation_strings":["School of Vehicle and Mobility, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3536-3501","affiliations":[{"raw_affiliation_string":"School of Vehicle and Mobility, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014512074","display_name":"Mengmeng Yang","orcid":"https://orcid.org/0000-0002-3294-6437"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengmeng Yang","raw_affiliation_strings":["School of Vehicle and Mobility, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Vehicle and Mobility, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012854401","display_name":"Fu Zheng","orcid":"https://orcid.org/0000-0001-6129-9251"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Fu","raw_affiliation_strings":["School of Vehicle and Mobility, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Vehicle and Mobility, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100735948","display_name":"Diange Yang","orcid":"https://orcid.org/0000-0002-0074-2448"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Diange Yang","raw_affiliation_strings":["School of Vehicle and Mobility, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Vehicle and Mobility, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.0037,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.76265975,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"52","issue":"3","first_page":"423","last_page":"434"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9995999932289124,"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/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/T12740","display_name":"Gait Recognition and Analysis","score":0.9961000084877014,"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":0.8035785555839539},{"id":"https://openalex.org/keywords/gesture","display_name":"Gesture","score":0.6394383907318115},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6271264553070068},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6041254997253418},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5911293625831604},{"id":"https://openalex.org/keywords/gesture-recognition","display_name":"Gesture recognition","score":0.5890982747077942},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5889267921447754},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47694578766822815},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44953352212905884},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3371240496635437}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8035785555839539},{"id":"https://openalex.org/C207347870","wikidata":"https://www.wikidata.org/wiki/Q371174","display_name":"Gesture","level":2,"score":0.6394383907318115},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6271264553070068},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6041254997253418},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5911293625831604},{"id":"https://openalex.org/C159437735","wikidata":"https://www.wikidata.org/wiki/Q1519524","display_name":"Gesture recognition","level":3,"score":0.5890982747077942},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5889267921447754},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47694578766822815},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44953352212905884},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3371240496635437},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/thms.2021.3121649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/thms.2021.3121649","pdf_url":null,"source":{"id":"https://openalex.org/S2476799526","display_name":"IEEE Transactions on Human-Machine Systems","issn_l":"2168-2291","issn":["2168-2291","2168-2305"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Human-Machine Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6100000143051147}],"awards":[{"id":"https://openalex.org/G7458066130","display_name":null,"funder_award_id":"U1864203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8518143839","display_name":null,"funder_award_id":"2019M660622","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":72,"referenced_works":["https://openalex.org/W56153741","https://openalex.org/W148014571","https://openalex.org/W1522734439","https://openalex.org/W1861492603","https://openalex.org/W1983364832","https://openalex.org/W1984798172","https://openalex.org/W2023651548","https://openalex.org/W2030061193","https://openalex.org/W2044799968","https://openalex.org/W2058256495","https://openalex.org/W2064675550","https://openalex.org/W2086509056","https://openalex.org/W2124688298","https://openalex.org/W2127740234","https://openalex.org/W2142203883","https://openalex.org/W2145546283","https://openalex.org/W2194775991","https://openalex.org/W2275395544","https://openalex.org/W2337434236","https://openalex.org/W2378645615","https://openalex.org/W2519750370","https://openalex.org/W2776652812","https://openalex.org/W2792345332","https://openalex.org/W2796633859","https://openalex.org/W2798644314","https://openalex.org/W2810685774","https://openalex.org/W2902214002","https://openalex.org/W2916798096","https://openalex.org/W2945143046","https://openalex.org/W2948058585","https://openalex.org/W2950568498","https://openalex.org/W2952587893","https://openalex.org/W2953032189","https://openalex.org/W2954146807","https://openalex.org/W2962730651","https://openalex.org/W2963076818","https://openalex.org/W2963218601","https://openalex.org/W2963446712","https://openalex.org/W2963465695","https://openalex.org/W2963524571","https://openalex.org/W2963781481","https://openalex.org/W2963820951","https://openalex.org/W2964184470","https://openalex.org/W2964304707","https://openalex.org/W2981311757","https://openalex.org/W2982442523","https://openalex.org/W2989748828","https://openalex.org/W2991376513","https://openalex.org/W3006529527","https://openalex.org/W3035050855","https://openalex.org/W3035083776","https://openalex.org/W3035382196","https://openalex.org/W3035574168","https://openalex.org/W3045471054","https://openalex.org/W3099014939","https://openalex.org/W3109652723","https://openalex.org/W3120927796","https://openalex.org/W3130641941","https://openalex.org/W3135617157","https://openalex.org/W3165135883","https://openalex.org/W3165265377","https://openalex.org/W3166932403","https://openalex.org/W3168851091","https://openalex.org/W4240042586","https://openalex.org/W4288100982","https://openalex.org/W4293584584","https://openalex.org/W4312245820","https://openalex.org/W6628877408","https://openalex.org/W6750227808","https://openalex.org/W6764611968","https://openalex.org/W6768765080","https://openalex.org/W6773520705"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W2537963312","https://openalex.org/W2902873204","https://openalex.org/W2185750513","https://openalex.org/W2010878661","https://openalex.org/W3147379364","https://openalex.org/W2026258298","https://openalex.org/W3204639664","https://openalex.org/W2970836791","https://openalex.org/W2989699735"],"abstract_inverted_index":{"Recognizing":[0],"traffic":[1,138,237],"command":[2,238],"gestures":[3],"with":[4,83,149,161,168,187,212],"high":[5],"accuracy":[6,182],"and":[7,68,96,101,165,183,190,216,225],"quick":[8],"response":[9,184],"at":[10,88],"a":[11,16,30,56,69,89,150,195,232],"low":[12,90],"computational":[13,91],"cost":[14,92],"is":[15,124,146,166,177],"requisite":[17],"for":[18,29],"driver":[19],"assistance":[20],"or":[21],"autonomous":[22],"driving.":[23],"However,":[24],"it":[25],"has":[26],"been":[27],"understudied":[28],"long":[31,70],"time.":[32],"Existing":[33],"research":[34,224],"takes":[35],"advantage":[36],"of":[37,115,153],"increasing":[38],"development":[39],"in":[40,98,118,130],"human":[41,64,86],"action":[42],"recognition":[43,60],"but":[44,58,93],"pays":[45],"little":[46],"attention":[47],"to":[48,106,126,157,179,228,235],"onboard":[49],"conditions.":[50],"In":[51],"this":[52],"article,":[53],"we":[54],"propose":[55],"simple":[57],"effective":[59],"model":[61],"based":[62],"on":[63],"upper-body":[65],"geometric":[66,77],"features":[67,78,164],"short-term":[71],"memory":[72],"(LSTM)":[73],"network.":[74],"The":[75,174],"handcrafted":[76,162],"can":[79,198],"easily":[80],"be":[81,199],"calculated":[82],"estimated":[84],"2-D":[85],"keypoints":[87],"are":[94,104,208],"discriminative":[95],"sufficient":[97],"classification.":[99],"Offline":[100],"online":[102,131],"inferences":[103],"implemented":[105],"comprehensively":[107],"evaluate":[108],"the":[109,113,119,128,134,143,147,217,220],"proposed":[110],"model.":[111],"For":[112],"sake":[114],"robustness":[116],"required":[117],"automotive":[120],"domain,":[121],"dual":[122],"voting":[123],"designed":[125],"filter":[127],"output":[129],"inference.":[132],"On":[133],"recently":[135],"published":[136],"Chinese":[137],"police":[139],"gesture":[140,239],"(CTPG)":[141],"dataset,":[142],"presented":[144],"approach":[145],"best":[148],"remarkable":[151],"improvement":[152],"approximately":[154],"8%":[155],"compared":[156],"previous":[158],"LSTM-based":[159],"methods":[160],"spatial":[163],"competitive":[167],"advanced":[169],"GCN-based":[170],"deep":[171],"learning":[172],"methods.":[173],"tradeoff":[175],"pattern":[176],"explored":[178],"demonstrate":[180],"how":[181],"time":[185],"alter":[186],"different":[188],"training":[189],"inference":[191],"strategies":[192],"so":[193],"that":[194],"balanced":[196],"setup":[197],"manually":[200],"chosen":[201],"under":[202],"various":[203],"application":[204,227],"scenarios.":[205],"Field":[206],"tests":[207],"also":[209],"carried":[210],"out":[211],"an":[213],"experimental":[214],"vehicle,":[215],"results":[218],"uncover":[219],"present":[221],"gap":[222],"between":[223],"practical":[226],"some":[229],"extent,":[230],"moving":[231],"step":[233],"closer":[234],"real-life":[236],"recognition.":[240]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}