{"id":"https://openalex.org/W2784165734","doi":"https://doi.org/10.1109/iscit.2017.8261171","title":"Unsupervised learning of space-time symmetric patterns in RGB-D videos for 4D human activity detection","display_name":"Unsupervised learning of space-time symmetric patterns in RGB-D videos for 4D human activity detection","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2784165734","doi":"https://doi.org/10.1109/iscit.2017.8261171","mag":"2784165734"},"language":"en","primary_location":{"id":"doi:10.1109/iscit.2017.8261171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscit.2017.8261171","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 17th International Symposium on Communications and Information Technologies (ISCIT)","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/A5038046608","display_name":"Yun-Jue Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I153512688","display_name":"National Taiwan Ocean University","ror":"https://ror.org/03bvvnt49","country_code":"TW","type":"education","lineage":["https://openalex.org/I153512688"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Yun-Jue Chen","raw_affiliation_strings":["Dept. of Computer & Information Engineering, National Taiwan Ocean University, Keelung, Taiwan"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer & Information Engineering, National Taiwan Ocean University, Keelung, Taiwan","institution_ids":["https://openalex.org/I153512688"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101839507","display_name":"Shyi\u2010Chyi Cheng","orcid":"https://orcid.org/0000-0003-4752-0460"},"institutions":[{"id":"https://openalex.org/I153512688","display_name":"National Taiwan Ocean University","ror":"https://ror.org/03bvvnt49","country_code":"TW","type":"education","lineage":["https://openalex.org/I153512688"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shyi-Chyi Cheng","raw_affiliation_strings":["Dept. of Computer & Information Engineering, National Taiwan Ocean University, Keelung, Taiwan"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer & Information Engineering, National Taiwan Ocean University, Keelung, Taiwan","institution_ids":["https://openalex.org/I153512688"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056155247","display_name":"Chen-Kuai Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I192703390","display_name":"Ming Chuan University","ror":"https://ror.org/02pgvzy25","country_code":"TW","type":"education","lineage":["https://openalex.org/I192703390"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chen-Kuai Yang","raw_affiliation_strings":["Dept. of Computer Science & Information Engineering, Ming Chan University, Taoyuan, Taiwan"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science & Information Engineering, Ming Chan University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I192703390"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5038046608"],"corresponding_institution_ids":["https://openalex.org/I153512688"],"apc_list":null,"apc_paid":null,"fwci":0.091,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.50263178,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"6"},"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":0.9973999857902527,"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/T12290","display_name":"Human Motion and Animation","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7765544652938843},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7138272523880005},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6305333971977234},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6112632155418396},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5893886685371399},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5805768370628357},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5094295144081116},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5041791200637817},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5024669170379639},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4960859715938568},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2241680920124054},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.20688968896865845}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7765544652938843},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7138272523880005},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6305333971977234},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6112632155418396},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5893886685371399},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5805768370628357},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5094295144081116},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5041791200637817},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5024669170379639},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4960859715938568},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2241680920124054},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.20688968896865845},{"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscit.2017.8261171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscit.2017.8261171","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 17th International Symposium on Communications and Information Technologies (ISCIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1923332106","https://openalex.org/W1945129080","https://openalex.org/W1945209586","https://openalex.org/W1981526027","https://openalex.org/W1994477568","https://openalex.org/W1996884424","https://openalex.org/W2042701174","https://openalex.org/W2081706464","https://openalex.org/W2090588508","https://openalex.org/W2099600129","https://openalex.org/W2103914106","https://openalex.org/W2114216982","https://openalex.org/W2120940063","https://openalex.org/W2159546866","https://openalex.org/W2181862262","https://openalex.org/W2291208916","https://openalex.org/W2296494999","https://openalex.org/W2472970127","https://openalex.org/W2578491468","https://openalex.org/W6677265961"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2145836866","https://openalex.org/W2803255133","https://openalex.org/W4310873165"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3,47,195],"present":[4],"an":[5,88,131],"approach":[6,216],"for":[7,207],"finding":[8],"space-time":[9],"activity":[10,27,89,107,122,132,136,147,154,160,170,181,205,236],"map":[11,90,182],"in":[12,36,100,108,133,211,233],"a":[13,24,62,101,109,119,150,179],"video":[14,22,34,45,52,64,102,110],"shot":[15],"using":[16,219],"3D":[17],"moment":[18],"methods.":[19],"A":[20],"RGB-D":[21,212],"involves":[23],"specific":[25],"human":[26,38,106,209],"is":[28,115,142,186],"first":[29],"regularly":[30],"partitioned":[31],"into":[32,50,149,174],"multiple":[33,51,113,175],"shots":[35,114],"which":[37,54,79],"activities":[39,210],"can":[40],"be":[41],"defined.":[42],"For":[43],"each":[44],"shot,":[46],"separate":[48],"it":[49],"cubes":[53],"characterizes":[55],"local":[56,63],"object":[57],"shape":[58],"and":[59,75,239],"motion.":[60],"Given":[61],"cube,":[65],"the":[66,73,93,127,138,146,159,163,167,190,226,230],"proposed":[67,215,231],"spacetime":[68],"pattern":[69],"detector":[70],"extracts":[71],"both":[72],"spatial":[74],"temporal":[76,128],"symmetric":[77],"information":[78],"are":[80,172],"further":[81],"grouped":[82],"together":[83],"by":[84,118],"hashing":[85],"to":[86,125,144,177,188,199],"construct":[87],"that":[91],"describes":[92],"distribution":[94],"of":[95,98,112,121,130,135,152,158,162,203,229,235],"motion":[96],"vectors":[97],"objects":[99],"shot.":[103],"The":[104,214],"intrinsic":[105],"consisting":[111],"finally":[116],"represented":[117],"set":[120,151],"maps.":[123,155],"Next,":[124],"reduce":[126],"dimensionality":[129],"terms":[134,234],"maps,":[137],"kernel":[139],"PCA":[140],"method":[141,232],"applied":[143],"transform":[145],"representation":[148],"principal":[153,169,180,204],"Finally,":[156],"regardless":[157],"types":[161],"training":[164,168],"videos,":[165],"all":[166],"maps":[171,206],"clustered":[173],"clusters":[176],"generate":[178],"dictionary.":[183],"This":[184],"dictionary":[185],"used":[187],"solve":[189],"initial":[191],"pose":[192],"problem":[193],"when":[194],"use":[196],"dynamic":[197],"programming":[198],"align":[200],"two":[201],"sequences":[202],"recognizing":[208],"videos.":[213],"was":[217],"tested":[218],"publicly":[220],"available":[221],"datasets.":[222],"Experimental":[223],"results":[224],"demonstrate":[225],"good":[227],"performance":[228],"detection":[237],"accuracy":[238],"execution":[240],"speed.":[241]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}