{"id":"https://openalex.org/W3198089975","doi":"https://doi.org/10.1109/iros51168.2021.9636553","title":"Robust Event Detection based on Spatio-Temporal Latent Action Unit using Skeletal Information","display_name":"Robust Event Detection based on Spatio-Temporal Latent Action Unit using Skeletal Information","publication_year":2021,"publication_date":"2021-09-27","ids":{"openalex":"https://openalex.org/W3198089975","doi":"https://doi.org/10.1109/iros51168.2021.9636553","mag":"3198089975"},"language":"en","primary_location":{"id":"doi:10.1109/iros51168.2021.9636553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros51168.2021.9636553","pdf_url":null,"source":{"id":"https://openalex.org/S4363607734","display_name":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://mediatum.ub.tum.de/1631405","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086922668","display_name":"Hao Xing","orcid":"https://orcid.org/0000-0001-8346-3454"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hao Xing","raw_affiliation_strings":["Machine Vision and Perception Group, Technical University of Munich, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Vision and Perception Group, Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101843710","display_name":"Yuxuan Xue","orcid":"https://orcid.org/0000-0002-7467-5540"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Yuxuan Xue","raw_affiliation_strings":["Machine Vision and Perception Group, Technical University of Munich, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Vision and Perception Group, Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061386768","display_name":"Mingchuan Zhou","orcid":"https://orcid.org/0000-0002-6944-1483"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mingchuan Zhou","raw_affiliation_strings":["Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028614249","display_name":"Darius Burschka","orcid":"https://orcid.org/0000-0002-9866-0343"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Darius Burschka","raw_affiliation_strings":["Machine Vision and Perception Group, Technical University of Munich, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Vision and Perception Group, Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I62916508"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2941","last_page":"2948"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9980000257492065,"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":0.9975000023841858,"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":0.7236566543579102},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7025960087776184},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6873478293418884},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.664451003074646},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5453022718429565},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5148317217826843},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5075260400772095},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5019872188568115},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48830342292785645},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.4502968192100525},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37171196937561035},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32663923501968384}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7236566543579102},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7025960087776184},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6873478293418884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.664451003074646},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5453022718429565},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5148317217826843},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5075260400772095},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5019872188568115},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48830342292785645},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.4502968192100525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37171196937561035},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32663923501968384},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iros51168.2021.9636553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros51168.2021.9636553","pdf_url":null,"source":{"id":"https://openalex.org/S4363607734","display_name":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},{"id":"pmh:oai:mediatum.ub.tum.de:node/1631405","is_oa":true,"landing_page_url":"https://mediatum.ub.tum.de/1631405","pdf_url":null,"source":{"id":"https://openalex.org/S4377196330","display_name":"mediaTUM  (Technical University of Munich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I62916508","host_organization_name":"Technical University of Munich","host_organization_lineage":["https://openalex.org/I62916508"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"ConferencePaper"}],"best_oa_location":{"id":"pmh:oai:mediatum.ub.tum.de:node/1631405","is_oa":true,"landing_page_url":"https://mediatum.ub.tum.de/1631405","pdf_url":null,"source":{"id":"https://openalex.org/S4377196330","display_name":"mediaTUM  (Technical University of Munich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I62916508","host_organization_name":"Technical University of Munich","host_organization_lineage":["https://openalex.org/I62916508"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"ConferencePaper"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320307771","display_name":"Bayer","ror":"https://ror.org/04hmn8g73"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W129703402","https://openalex.org/W1973464566","https://openalex.org/W1992405901","https://openalex.org/W1996489823","https://openalex.org/W2011847543","https://openalex.org/W2026350744","https://openalex.org/W2028456138","https://openalex.org/W2040281784","https://openalex.org/W2085261163","https://openalex.org/W2096300942","https://openalex.org/W2100716893","https://openalex.org/W2112447569","https://openalex.org/W2122330587","https://openalex.org/W2124053023","https://openalex.org/W2125838338","https://openalex.org/W2129666410","https://openalex.org/W2138407306","https://openalex.org/W2142258645","https://openalex.org/W2151078206","https://openalex.org/W2155268664","https://openalex.org/W2157785665","https://openalex.org/W2162670331","https://openalex.org/W2166786963","https://openalex.org/W2519911873","https://openalex.org/W2560119893","https://openalex.org/W2617323512","https://openalex.org/W2745471877","https://openalex.org/W2766682667","https://openalex.org/W2773326826","https://openalex.org/W2781497391","https://openalex.org/W2794956979","https://openalex.org/W2806443522","https://openalex.org/W2883710612","https://openalex.org/W2884571892","https://openalex.org/W2899146968","https://openalex.org/W2905571172","https://openalex.org/W2913397815","https://openalex.org/W2914828435","https://openalex.org/W2963076818","https://openalex.org/W2963505807","https://openalex.org/W2963855530","https://openalex.org/W2964134613","https://openalex.org/W2968993603","https://openalex.org/W2973137164","https://openalex.org/W2973258311","https://openalex.org/W2980662037","https://openalex.org/W2994721977","https://openalex.org/W2999794487","https://openalex.org/W3006905737","https://openalex.org/W3048048346","https://openalex.org/W3049455300","https://openalex.org/W3104290757","https://openalex.org/W3117784098","https://openalex.org/W3131579999","https://openalex.org/W6676727762","https://openalex.org/W6781617550","https://openalex.org/W6782622061","https://openalex.org/W6787768197"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W4230315250","https://openalex.org/W2086519370","https://openalex.org/W2087343574"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,41,72,105,124,149],"novel":[4],"dictionary":[5,185],"learning":[6,186],"approach":[7,167],"to":[8,39,44,104],"detect":[9],"event":[10,20,111,140,179],"anomalities":[11],"using":[12],"skeletal":[13],"information":[14],"extracted":[15],"from":[16,46,113],"RGBD":[17],"video.":[18],"The":[19,56],"action":[21,27,112,120],"is":[22,69,101],"represented":[23],"as":[24],"several":[25],"latent":[26,32,125],"atoms":[28],"and":[29,34,49,76,116,164,174,194],"composed":[30],"of":[31,138,159,176],"spatial":[33],"temporal":[35,127],"attributes.":[36],"We":[37,154],"aim":[38],"construct":[40],"network":[42],"able":[43],"learn":[45],"few":[47],"examples":[48],"also":[50],"rules":[51],"defined":[52],"by":[53,61,96],"the":[54,87,98,110,133,136,156,160,169,191,195],"user.":[55],"skeleton":[57,67],"frames":[58,91],"are":[59],"clustered":[60],"an":[62],"initial":[63],"K-means":[64],"method.":[65],"Each":[66],"frame":[68],"assigned":[70],"with":[71,182,198],"varying":[73],"weight":[74,99],"parameter":[75],"fed":[77],"into":[78],"our":[79],"Gradual":[80],"Online":[81],"Dictionary":[82],"Learning":[83],"(GODL)":[84],"algorithm.":[85],"During":[86],"training":[88],"process,":[89],"outlier":[90],"will":[92],"be":[93],"gradually":[94],"filtered":[95],"reducing":[97],"that":[100],"inversely":[102],"proportional":[103],"cost.":[106],"To":[107],"strictly":[108],"distinguish":[109],"similar":[114],"actions":[115],"robustly":[117],"acquire":[118],"its":[119],"units,":[121],"we":[122],"build":[123],"unit":[126],"structure":[128],"for":[129,152],"each":[130],"sub-action.We":[131],"validate":[132],"method":[134,189],"at":[135],"example":[137],"fall":[139,178],"detection":[141],"on":[142],"NTU":[143],"RGB+D":[144],"dataset,":[145],"because":[146],"it":[147],"provides":[148],"benchmark":[150],"available":[151],"comparison.":[153],"present":[155],"experimental":[157],"validation":[158],"achieved":[161],"accuracy,":[162],"recall,":[163],"precision.":[165],"Our":[166,188],"achieves":[168],"best":[170],"performance":[171],"in":[172],"precision":[173],"accuracy":[175,193],"human":[177],"detection,":[180],"compared":[181],"other":[183],"existing":[184],"methods.":[187],"remains":[190],"highest":[192],"lowest":[196],"variance,":[197],"increasing":[199],"noise":[200],"ratio.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}