{"id":"https://openalex.org/W2751329796","doi":"https://doi.org/10.1109/icmew.2017.8026292","title":"Hierarchical pedestrian attribute recognition based on adaptive region localization","display_name":"Hierarchical pedestrian attribute recognition based on adaptive region localization","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2751329796","doi":"https://doi.org/10.1109/icmew.2017.8026292","mag":"2751329796"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2017.8026292","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2017.8026292","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia &amp; Expo Workshops (ICMEW)","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/A5052163728","display_name":"Chunfeng Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chunfeng Yao","raw_affiliation_strings":["Noah's Ark Laboratory, Huawei Technologies Co., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Noah's Ark Laboratory, Huawei Technologies Co., Ltd., Beijing, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039248257","display_name":"Bailan Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bailan Feng","raw_affiliation_strings":["Noah's Ark Laboratory, Huawei Technologies Co., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Noah's Ark Laboratory, Huawei Technologies Co., Ltd., Beijing, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100733931","display_name":"Defeng Li","orcid":"https://orcid.org/0000-0002-9316-3313"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Defeng Li","raw_affiliation_strings":["Noah's Ark Laboratory, Huawei Technologies Co., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Noah's Ark Laboratory, Huawei Technologies Co., Ltd., Beijing, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023638809","display_name":"Jian Li","orcid":"https://orcid.org/0000-0001-5880-2565"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Li","raw_affiliation_strings":["Noah's Ark Laboratory, Huawei Technologies Co., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Noah's Ark Laboratory, Huawei Technologies Co., Ltd., Beijing, China","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5052163728"],"corresponding_institution_ids":["https://openalex.org/I2250955327"],"apc_list":null,"apc_paid":null,"fwci":0.3641,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.68781082,"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":"471","last_page":"476"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9973999857902527,"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/pedestrian","display_name":"Pedestrian","score":0.7946622371673584},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7438063621520996},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6342113018035889},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5210185050964355},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.46729496121406555},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.45115411281585693},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4464518129825592},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.42817527055740356},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4279424846172333},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3798466920852661},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32246869802474976},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1425398886203766},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06841957569122314}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7946622371673584},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7438063621520996},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6342113018035889},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5210185050964355},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.46729496121406555},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.45115411281585693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4464518129825592},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.42817527055740356},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4279424846172333},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3798466920852661},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32246869802474976},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1425398886203766},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06841957569122314},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmew.2017.8026292","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2017.8026292","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia &amp; Expo Workshops (ICMEW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W262462045","https://openalex.org/W1522973599","https://openalex.org/W1958932515","https://openalex.org/W1967988963","https://openalex.org/W1999705173","https://openalex.org/W2009363625","https://openalex.org/W2047314391","https://openalex.org/W2093121049","https://openalex.org/W2101392314","https://openalex.org/W2111025459","https://openalex.org/W2120990504","https://openalex.org/W2128560777","https://openalex.org/W2134313025","https://openalex.org/W2147414309","https://openalex.org/W2147507563","https://openalex.org/W2149965764","https://openalex.org/W2163605009","https://openalex.org/W2168231600","https://openalex.org/W2271654614","https://openalex.org/W2286727787","https://openalex.org/W2308869522","https://openalex.org/W2410968923","https://openalex.org/W2519904008","https://openalex.org/W6631255387","https://openalex.org/W6684191040","https://openalex.org/W6684859321","https://openalex.org/W6714781226","https://openalex.org/W6726373078"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2101960027","https://openalex.org/W2197846993","https://openalex.org/W49697837","https://openalex.org/W2586575957","https://openalex.org/W3122828758","https://openalex.org/W2170799233","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"Learning":[0],"to":[1,20,38,71],"recognize":[2],"pedestrian":[3,63,88],"attributes":[4,89],"(such":[5,93,101],"as":[6,26,90,94,102],"gender,":[7],"hair":[8,103],"style,":[9],"take":[10],"hat":[11],"or":[12],"not)":[13],"in":[14,44,49],"video":[15],"surveillance":[16,47,74,169],"scenarios":[17,170],"is":[18,34,112],"critical":[19],"a":[21,61,80],"variety":[22],"of":[23,175],"tasks,":[24],"such":[25],"crime":[27],"prevention":[28],"and":[29,41,96,98,105,119,152,166],"border":[30],"control.":[31],"However,":[32],"it":[33],"still":[35],"challenging":[36],"due":[37],"low":[39],"resolution":[40],"highlight":[42],"influence":[43],"the":[45,72,87,109,115,120,126,132,173,176],"actual":[46,73,168],"scenarios,":[48],"which":[50,67],"traditional":[51],"methods":[52],"work":[53],"not":[54],"well.":[55],"This":[56],"paper":[57],"aims":[58],"at":[59],"proposing":[60],"robust":[62],"attribute":[64,117,128],"recognition":[65,82],"framework":[66],"can":[68],"be":[69],"adaptive":[70,140],"scenarios.":[75],"Specifically,":[76],"we":[77,136],"first":[78],"propose":[79,138],"hierarchical":[81],"strategy":[83],"by":[84],"heuristically":[85],"classifying":[86],"global":[91,116],"ones":[92,100],"gender":[95],"age)":[97],"local":[99,127],"style":[104],"has":[106],"glass).":[107],"Then":[108],"whole":[110],"region":[111,141,154],"used":[113,124],"for":[114,125],"recognition,":[118],"relevant":[121,133,153],"regions":[122,134],"are":[123],"recognition.":[129],"To":[130],"estimate":[131],"above,":[135],"further":[137],"an":[139],"localization":[142,155],"scheme,":[143],"including":[144],"position":[145],"estimation":[146],"based":[147,156],"on":[148,157,163],"geometric":[149],"human":[150],"body":[151],"random":[158],"expansion.":[159],"Finally,":[160],"experimental":[161],"results":[162],"representative":[164],"datasets":[165],"our":[167],"both":[171],"demonstrate":[172],"effectiveness":[174],"proposed":[177],"method.":[178]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
