{"id":"https://openalex.org/W3114162974","doi":"https://doi.org/10.1109/gcce50665.2020.9292004","title":"Privacy-aware User Watching System using 2D Pose Estimation","display_name":"Privacy-aware User Watching System using 2D Pose Estimation","publication_year":2020,"publication_date":"2020-10-13","ids":{"openalex":"https://openalex.org/W3114162974","doi":"https://doi.org/10.1109/gcce50665.2020.9292004","mag":"3114162974"},"language":"en","primary_location":{"id":"doi:10.1109/gcce50665.2020.9292004","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce50665.2020.9292004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 9th Global Conference on Consumer Electronics (GCCE)","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/A5109425752","display_name":"Takeshi Konno","orcid":null},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takeshi Konno","raw_affiliation_strings":["Fujitsu Laboratories Ltd. 4-1-1, Kamikodanaka, Nakahara-ku, Kawasaki, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Laboratories Ltd. 4-1-1, Kamikodanaka, Nakahara-ku, Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066480164","display_name":"Shuji Awai","orcid":"https://orcid.org/0009-0005-1812-8170"},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shuji Awai","raw_affiliation_strings":["Fujitsu Laboratories Ltd. 4-1-1, Kamikodanaka, Nakahara-ku, Kawasaki, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Laboratories Ltd. 4-1-1, Kamikodanaka, Nakahara-ku, Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022610591","display_name":"Megumi Chikano","orcid":"https://orcid.org/0009-0000-9967-8759"},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Megumi Chikano","raw_affiliation_strings":["Fujitsu Laboratories Ltd. 4-1-1, Kamikodanaka, Nakahara-ku, Kawasaki, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Laboratories Ltd. 4-1-1, Kamikodanaka, Nakahara-ku, Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2252096349"],"apc_list":null,"apc_paid":null,"fwci":0.0853,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.41327018,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"314","last_page":"315"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":1.0,"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"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9950000047683716,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9896000027656555,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7921251058578491},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.7480078339576721},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.7289009690284729},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5785765647888184},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5321561098098755},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5081298351287842},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43737122416496277},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.43589988350868225},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.36401838064193726},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34369122982025146},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3392711281776428}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7921251058578491},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.7480078339576721},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.7289009690284729},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5785765647888184},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5321561098098755},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5081298351287842},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43737122416496277},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.43589988350868225},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.36401838064193726},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34369122982025146},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3392711281776428},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C42407357","wikidata":"https://www.wikidata.org/wiki/Q521","display_name":"Physiology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/gcce50665.2020.9292004","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce50665.2020.9292004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 9th Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6800000071525574,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W2126680226","https://openalex.org/W2322772590"],"related_works":["https://openalex.org/W2123263858","https://openalex.org/W3127959533","https://openalex.org/W2894986065","https://openalex.org/W4387967917","https://openalex.org/W4287600488","https://openalex.org/W4386925306","https://openalex.org/W4387968151","https://openalex.org/W3132124459","https://openalex.org/W2946083937","https://openalex.org/W2386960251"],"abstract_inverted_index":{"In":[0,143,159,223,279],"the":[1,4,33,69,75,78,81,105,108,144,155,160,171,197,204,228,235,266,270,280,286],"super-aged":[2],"society,":[3],"number":[5],"of":[6,32,38,80,110,117,190,216,231,243,248,268],"elderly":[7,54,249],"people":[8,55,250],"who":[9],"go":[10],"missing":[11,34],"due":[12],"to":[13,48,102,149,165,185,195,260],"dementia":[14],"is":[15,124,147,157,163,173,183,193,218],"increasing":[16],"year":[17],"by":[18,52],"year.":[19],"The":[20,175],"police":[21],"and":[22,41,73,134,154,170,265],"local":[23],"governments":[24],"are":[25,43,46,84,88],"speeding":[26],"up":[27],"efforts":[28],"for":[29,220],"early":[30],"detection":[31,40],"people,":[35],"because":[36],"cases":[37],"late":[39],"death":[42],"increasing.":[44],"There":[45,87],"systems":[47],"collect":[49,186],"location":[50],"information":[51,92,106,123,141],"having":[53],"carry":[56,63],"smartphones,":[57],"etc.,":[58],"but":[59,97,180],"they":[60],"don't":[61],"always":[62],"these":[64],"devices":[65],"with":[66,251,273,293],"them.":[67],"Therefore,":[68],"mechanisms":[70],"which":[71,207],"matches":[72],"tracks":[74],"person":[76,94],"from":[77,107,152,168,212],"images":[79],"security":[82,294],"cameras":[83],"under":[85],"consideration.":[86],"methods":[89,120,276],"using":[90,104,121,137,234,245],"face":[91],"as":[93,113,240],"matching":[95,114,136,233],"technologies,":[96],"many":[98],"countries":[99],"have":[100],"started":[101],"avoid":[103],"viewpoint":[109],"privacy.":[111],"Recently,":[112],"technologies":[115],"capable":[116],"privacy":[118,291],"protection,":[119],"gait":[122,129,135,182,221,232],"noticed.":[125],"Typical":[126],"studies":[127],"include":[128],"energy":[130],"imaging":[131],"(GEI)":[132],"[1-2]":[133],"2D":[138],"pose":[139],"estimation":[140],"[3].":[142],"former,":[145,161],"it":[146,162,192],"difficult":[148,164,184,194],"separate":[150,166],"human":[151,167],"background,":[153,169],"accuracy":[156,172,271],"low.":[158,174],"latter":[176],"uses":[177],"deep":[178],"learning,":[179],"since":[181],"a":[187,213,241],"large":[188],"amount":[189,215],"data,":[191,217],"create":[196],"highly":[198],"accurate":[199],"model.":[200],"We":[201],"considered":[202],"that":[203,289],"rule-based":[205],"method,":[206],"can":[208],"design":[209],"feature":[210,237],"quantities":[211],"small":[214],"suitable":[219],"matching.":[222],"this":[224,255],"paper,":[225],"we":[226],"propose":[227],"new":[229],"method":[230,257],"designed":[236],"quantity.":[238],"And,":[239],"result":[242,267],"evaluating":[244],"walking":[246],"image":[247],"different":[252],"photographing":[253],"date,":[254],"proposed":[256],"was":[258,277],"able":[259],"confirm":[261],"at":[262],"82.9%":[263],"accuracy,":[264],"improving":[269],"compared":[272],"several":[274],"conventional":[275],"obtained.":[278],"future,":[281],"further":[282],"robustness":[283],"will":[284],"enable":[285],"watching":[287],"system":[288],"protect":[290],"even":[292],"cameras.":[295]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}