{"id":"https://openalex.org/W2106169946","doi":"https://doi.org/10.1109/roman.2014.6926260","title":"A two-layered approach to recognize high-level human activities","display_name":"A two-layered approach to recognize high-level human activities","publication_year":2014,"publication_date":"2014-08-01","ids":{"openalex":"https://openalex.org/W2106169946","doi":"https://doi.org/10.1109/roman.2014.6926260","mag":"2106169946"},"language":"en","primary_location":{"id":"doi:10.1109/roman.2014.6926260","is_oa":false,"landing_page_url":"https://doi.org/10.1109/roman.2014.6926260","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 23rd IEEE International Symposium on Robot and Human Interactive Communication","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/A5085312908","display_name":"Ninghang Hu","orcid":"https://orcid.org/0000-0001-6831-4653"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Ninghang Hu","raw_affiliation_strings":["Intelligent System Lab Amsterdam, University of Amsterdam, Amsterdam, The Netherlans","Intelligent System Lab Amsterdam, University of Amsterdam, 1098XH, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Intelligent System Lab Amsterdam, University of Amsterdam, Amsterdam, The Netherlans","institution_ids":["https://openalex.org/I887064364"]},{"raw_affiliation_string":"Intelligent System Lab Amsterdam, University of Amsterdam, 1098XH, The Netherlands","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074888054","display_name":"Gwenn Englebienne","orcid":"https://orcid.org/0000-0002-3130-2082"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Gwenn Englebienne","raw_affiliation_strings":["Intelligent System Lab Amsterdam, University of Amsterdam, Amsterdam, The Netherlans","Intelligent System Lab Amsterdam, University of Amsterdam, 1098XH, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Intelligent System Lab Amsterdam, University of Amsterdam, Amsterdam, The Netherlans","institution_ids":["https://openalex.org/I887064364"]},{"raw_affiliation_string":"Intelligent System Lab Amsterdam, University of Amsterdam, 1098XH, The Netherlands","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049349937","display_name":"Ben Kr\u00f6se","orcid":"https://orcid.org/0000-0003-1237-0618"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]},{"id":"https://openalex.org/I55106644","display_name":"Amsterdam University of Applied Sciences","ror":"https://ror.org/00y2z2s03","country_code":"NL","type":"education","lineage":["https://openalex.org/I55106644"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ben Krose","raw_affiliation_strings":["Amsterdam University of Applied Science","Intelligent System Lab Amsterdam, University of Amsterdam, 1098XH, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Amsterdam University of Applied Science","institution_ids":["https://openalex.org/I55106644"]},{"raw_affiliation_string":"Intelligent System Lab Amsterdam, University of Amsterdam, 1098XH, The Netherlands","institution_ids":["https://openalex.org/I887064364"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5085312908"],"corresponding_institution_ids":["https://openalex.org/I887064364"],"apc_list":null,"apc_paid":null,"fwci":1.4823,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.86289934,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"243","last_page":"248"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9995999932289124,"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":0.9995999932289124,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9994000196456909,"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":0.9961000084877014,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7956540584564209},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.7849789261817932},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7205472588539124},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.6191263198852539},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6134018898010254},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.598447859287262},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5952003002166748},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5262728929519653},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5003323554992676},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4799076020717621},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4780559837818146},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.4777633249759674},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.46912240982055664},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.45613107085227966}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7956540584564209},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.7849789261817932},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7205472588539124},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6191263198852539},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6134018898010254},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.598447859287262},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5952003002166748},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5262728929519653},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5003323554992676},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4799076020717621},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4780559837818146},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.4777633249759674},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.46912240982055664},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.45613107085227966},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/roman.2014.6926260","is_oa":false,"landing_page_url":"https://doi.org/10.1109/roman.2014.6926260","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 23rd IEEE International Symposium on Robot and Human Interactive Communication","raw_type":"proceedings-article"},{"id":"pmh:oai:dare.uva.nl:openaire_cris_publications/64529f27-650f-4549-8c60-4e9018796c5c","is_oa":false,"landing_page_url":"https://handle.uba.uva.nl/personal/pure/en/publications/a-twolayered-approach-to-recognize-highlevel-human-activities(64529f27-650f-4549-8c60-4e9018796c5c).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Hu, N, Englebienne, G & Kr\u00f6se, B 2014, A two-layered approach to recognize high-level human activities. in 2014 IEEE RO-MAN : the 23rd IEEE International Symposium on Robot and Human Interactive Communication : August 25-29, 2014 Edinburgh, Scotland, UK. Piscataway, NJ, pp. 243-248, IEEE RO-MAN 2014: The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 25/08/14. https://doi.org/10.1109/ROMAN.2014.6926260","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:dare.uva.nl:publications/64529f27-650f-4549-8c60-4e9018796c5c","is_oa":false,"landing_page_url":"http://dare.uva.nl/personal/pure/en/publications/a-twolayered-approach-to-recognize-highlevel-human-activities(64529f27-650f-4549-8c60-4e9018796c5c).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Hu, N, Englebienne, G & Kr\u00f6se, B 2014, A two-layered approach to recognize high-level human activities. in 2014 IEEE RO-MAN : the 23rd IEEE International Symposium on Robot and Human Interactive Communication : August 25-29, 2014 Edinburgh, Scotland, UK. Piscataway, NJ, pp. 243-248, IEEE RO-MAN 2014: The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 25/08/14. https://doi.org/10.1109/ROMAN.2014.6926260","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:uvapub:oai:dare.uva.nl:publications/64529f27-650f-4549-8c60-4e9018796c5c","is_oa":false,"landing_page_url":"https://dare.uva.nl/personal/pure/en/publications/a-twolayered-approach-to-recognize-highlevel-human-activities(64529f27-650f-4549-8c60-4e9018796c5c).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2014 IEEE RO-MAN: the 23rd IEEE International Symposium on Robot and Human Interactive Communication : August 25-29, 2014 Edinburgh, Scotland, UK, 243 - 248","raw_type":"info:eu-repo/semantics/conferencepaper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1530378878","https://openalex.org/W1976794186","https://openalex.org/W1978511849","https://openalex.org/W1983705368","https://openalex.org/W1984912494","https://openalex.org/W1988283640","https://openalex.org/W2014914041","https://openalex.org/W2017098439","https://openalex.org/W2017695267","https://openalex.org/W2057067088","https://openalex.org/W2069808690","https://openalex.org/W2105842272","https://openalex.org/W2110142955","https://openalex.org/W2119004998","https://openalex.org/W2133254423","https://openalex.org/W2142194269","https://openalex.org/W2142258645","https://openalex.org/W2146055337","https://openalex.org/W2147102238","https://openalex.org/W2147196093","https://openalex.org/W2155015876","https://openalex.org/W2160517719","https://openalex.org/W2160551847","https://openalex.org/W2161315665","https://openalex.org/W2165051227","https://openalex.org/W2268284457","https://openalex.org/W2293288132","https://openalex.org/W2533503513","https://openalex.org/W4285719527","https://openalex.org/W6628868384","https://openalex.org/W6647496649","https://openalex.org/W6655019712","https://openalex.org/W6664757799","https://openalex.org/W6675783020","https://openalex.org/W6676671045","https://openalex.org/W6677921164","https://openalex.org/W6681785522"],"related_works":["https://openalex.org/W3195649134","https://openalex.org/W2281498195","https://openalex.org/W2017526120","https://openalex.org/W2610664080","https://openalex.org/W2188304107","https://openalex.org/W2761510556","https://openalex.org/W2892259437","https://openalex.org/W2944566775","https://openalex.org/W2131801795","https://openalex.org/W2166927590"],"abstract_inverted_index":{"Automated":[0],"human":[1],"activity":[2,15],"recognition":[3,16],"is":[4,76,100,125],"an":[5,19],"essential":[6],"task":[7],"for":[8,70],"Human":[9],"Robot":[10],"Interaction":[11],"(HRI).":[12],"A":[13],"successful":[14],"system":[17],"enables":[18],"assistant":[20],"robot":[21],"to":[22,127],"provide":[23],"precise":[24],"services.":[25],"In":[26,43,57],"this":[27],"paper,":[28],"we":[29,61],"present":[30],"a":[31,79,87,103],"two-layered":[32],"approach":[33,112,143],"that":[34,83,109,118,137],"can":[35,85],"recognize":[36],"sub-level":[37,91],"activities":[38,41,49,66],"and":[39,147],"high-level":[40,72],"successively.":[42],"the":[44,47,54,58,63,95,110,114,119,123,130,141],"first":[45],"layer,":[46,60],"low-level":[48,65],"are":[50],"recognized":[51,64],"based":[52],"on":[53,102],"RGB-D":[55],"video.":[56],"second":[59],"use":[62],"as":[67],"input":[68],"features":[69],"estimating":[71],"activities.":[73],"Our":[74,98],"model":[75,99,124,139],"embedded":[77],"with":[78,94],"latent":[80],"node,":[81],"so":[82],"it":[84],"capture":[86],"richer":[88],"class":[89],"of":[90,122],"semantics":[92],"compared":[93],"traditional":[96],"approach.":[97],"evaluated":[101],"challenging":[104],"benchmark":[105],"dataset.":[106],"We":[107],"show":[108,136],"proposed":[111],"outperforms":[113,140],"single-layered":[115],"approach,":[116],"suggesting":[117],"hierarchical":[120],"nature":[121],"able":[126],"better":[128],"explain":[129],"observed":[131],"data.":[132],"The":[133],"results":[134],"also":[135],"our":[138],"state-of-the-art":[142],"in":[144],"accuracy,":[145],"precision":[146],"recall.":[148]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
