{"id":"https://openalex.org/W2322772590","doi":"https://doi.org/10.1109/tpami.2016.2545669","title":"A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs","display_name":"A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs","publication_year":2016,"publication_date":"2016-03-23","ids":{"openalex":"https://openalex.org/W2322772590","doi":"https://doi.org/10.1109/tpami.2016.2545669","mag":"2322772590","pmid":"https://pubmed.ncbi.nlm.nih.gov/27019478"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2016.2545669","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2016.2545669","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5101539923","display_name":"Zifeng Wu","orcid":"https://orcid.org/0000-0002-0990-233X"},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I5681781"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["AU","CN"],"is_corresponding":true,"raw_author_name":"Zifeng Wu","raw_affiliation_strings":["National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China","University of Adelaide"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Adelaide","institution_ids":["https://openalex.org/I5681781"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024579621","display_name":"Yongzhen Huang","orcid":"https://orcid.org/0000-0003-4389-9805"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongzhen Huang","raw_affiliation_strings":["National Laboratory of Pattern Recognition, Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition, Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115602506","display_name":"Liang Wang","orcid":"https://orcid.org/0000-0001-5224-8647"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Wang","raw_affiliation_strings":["National Laboratory of Pattern Recognition, Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition, Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100444820","display_name":"Xiaogang Wang","orcid":"https://orcid.org/0000-0002-7929-5889"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaogang Wang","raw_affiliation_strings":["Chinese University of Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Chinese University of Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111885963","display_name":"Tieniu Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tieniu Tan","raw_affiliation_strings":["National Laboratory of Pattern Recognition, Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition, Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101539923"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150","https://openalex.org/I5681781"],"apc_list":null,"apc_paid":null,"fwci":28.2578,"has_fulltext":false,"cited_by_count":730,"citation_normalized_percentile":{"value":0.99842124,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"39","issue":"2","first_page":"209","last_page":"226"},"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":0.9879000186920166,"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/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.7559939622879028},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7555243968963623},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7177294492721558},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6765645742416382},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6335679292678833},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6267111301422119},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6034771203994751},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5170627236366272},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5160831809043884},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5076662302017212},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49336567521095276},{"id":"https://openalex.org/keywords/cross-validation","display_name":"Cross-validation","score":0.4864818751811981},{"id":"https://openalex.org/keywords/gait-analysis","display_name":"Gait analysis","score":0.4500027000904083},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.42690038681030273},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3907069265842438},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1592405140399933},{"id":"https://openalex.org/keywords/physical-medicine-and-rehabilitation","display_name":"Physical medicine and rehabilitation","score":0.11516013741493225},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1121370792388916}],"concepts":[{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.7559939622879028},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7555243968963623},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7177294492721558},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6765645742416382},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6335679292678833},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6267111301422119},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6034771203994751},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5170627236366272},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5160831809043884},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5076662302017212},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49336567521095276},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.4864818751811981},{"id":"https://openalex.org/C173906292","wikidata":"https://www.wikidata.org/wiki/Q1493441","display_name":"Gait analysis","level":3,"score":0.4500027000904083},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.42690038681030273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3907069265842438},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1592405140399933},{"id":"https://openalex.org/C99508421","wikidata":"https://www.wikidata.org/wiki/Q2678675","display_name":"Physical medicine and rehabilitation","level":1,"score":0.11516013741493225},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1121370792388916},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"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/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tpami.2016.2545669","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2016.2545669","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:27019478","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/27019478","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null},{"id":"pmh:oai:digital.library.adelaide.edu.au:2440/110135","is_oa":false,"landing_page_url":"http://hdl.handle.net/2440/110135","pdf_url":null,"source":{"id":"https://openalex.org/S4306401835","display_name":"Adelaide Research & Scholarship (AR&S) (University of Adelaide)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I5681781","host_organization_name":"The University of Adelaide","host_organization_lineage":["https://openalex.org/I5681781"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://dx.doi.org/10.1109/tpami.2016.2545669","raw_type":"Journal article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1354117261","display_name":null,"funder_award_id":"2012CB316300","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G5858177368","display_name":null,"funder_award_id":"61525306","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6132059711","display_name":null,"funder_award_id":"61420106015","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W345900524","https://openalex.org/W435670374","https://openalex.org/W846669277","https://openalex.org/W1487322600","https://openalex.org/W1487583988","https://openalex.org/W1536680647","https://openalex.org/W1546677826","https://openalex.org/W1561782558","https://openalex.org/W1665214252","https://openalex.org/W1782590233","https://openalex.org/W1896402587","https://openalex.org/W1903029394","https://openalex.org/W1904365287","https://openalex.org/W1969443303","https://openalex.org/W1971239273","https://openalex.org/W1983364832","https://openalex.org/W1987579743","https://openalex.org/W1989516595","https://openalex.org/W1998808035","https://openalex.org/W2000059041","https://openalex.org/W2013076218","https://openalex.org/W2015861736","https://openalex.org/W2016053056","https://openalex.org/W2016327746","https://openalex.org/W2018331988","https://openalex.org/W2022508996","https://openalex.org/W2038118117","https://openalex.org/W2058897916","https://openalex.org/W2059901520","https://openalex.org/W2060487848","https://openalex.org/W2063626761","https://openalex.org/W2064675550","https://openalex.org/W2068715223","https://openalex.org/W2072510697","https://openalex.org/W2084796520","https://openalex.org/W2098364470","https://openalex.org/W2100603595","https://openalex.org/W2104335344","https://openalex.org/W2104769793","https://openalex.org/W2105101328","https://openalex.org/W2112477260","https://openalex.org/W2113325037","https://openalex.org/W2116800718","https://openalex.org/W2117539524","https://openalex.org/W2121295188","https://openalex.org/W2126680226","https://openalex.org/W2131774270","https://openalex.org/W2133444763","https://openalex.org/W2145287260","https://openalex.org/W2148568366","https://openalex.org/W2151458682","https://openalex.org/W2154171558","https://openalex.org/W2154579312","https://openalex.org/W2156303437","https://openalex.org/W2157364932","https://openalex.org/W2162915993","https://openalex.org/W2163763295","https://openalex.org/W2618530766","https://openalex.org/W2795923793","https://openalex.org/W2963542991","https://openalex.org/W6629032133","https://openalex.org/W6629368666","https://openalex.org/W6633663260","https://openalex.org/W6637242042","https://openalex.org/W6640036494","https://openalex.org/W6675575696","https://openalex.org/W6682274878","https://openalex.org/W6682751323","https://openalex.org/W6682864246"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W1482209366","https://openalex.org/W2521627374"],"abstract_inverted_index":{"This":[0],"paper":[1],"studies":[2],"an":[3,75],"approach":[4],"to":[5,35,173,178],"gait":[6,42,67,109,171,192,234,237],"based":[7,62],"human":[8,27,49],"identification":[9],"via":[10],"similarity":[11],"learning":[12],"by":[13,121],"deep":[14,33,64],"convolutional":[15],"neural":[16],"networks":[17,34],"(CNNs).":[18],"With":[19],"a":[20,122,242],"pretty":[21],"small":[22],"group":[23],"of":[24,41,48,54,81,107,203,251],"labeled":[25],"multi-view":[26],"walking":[28],"videos,":[29],"we":[30,73],"can":[31,148],"train":[32],"recognize":[36],"the":[37,46,52,59,70,101,117,132,144,155,169,184,189,200,215,225,229,232],"most":[38],"discriminative":[39],"changes":[40],"patterns":[43],"which":[44],"suggest":[45],"change":[47],"identity.":[50],"To":[51],"best":[53,157,230],"our":[55,127,204],"knowledge,":[56],"this":[57,198,252],"is":[58,97,135,165,182,210,220],"first":[60,98],"work":[61],"on":[63,100,168,231],"CNNs":[65],"for":[66,191,217,254],"recognition":[68,146],"in":[69,79,105,188,241],"literature.":[71],"Here,":[72],"provide":[74],"extensive":[76],"empirical":[77],"evaluation":[78],"terms":[80,106],"various":[82],"scenarios,":[83],"namely,":[84],"cross-view":[85,108,133,218],"and":[86,92,214],"cross-walking-condition,":[87],"with":[88,194],"different":[89],"preprocessing":[90],"approaches":[91],"network":[93],"architectures.":[94],"The":[95,163],"method":[96,128,164,205,226,253],"evaluated":[99,167],"challenging":[102],"CASIA-B":[103],"dataset":[104,172,186],"recognition.":[110],"Experimental":[111],"results":[112,247],"show":[113,248],"that":[114],"it":[115],"outperforms":[116],"previous":[118,156],"state-of-the-art":[119],"methods":[120],"significant":[123],"margin.":[124],"In":[125],"particular,":[126],"shows":[129],"advantages":[130],"when":[131],"angle":[134],"large,":[136],"i.e.,":[137],"no":[138],"less":[139],"than":[140,154,160],"36":[141],"degree.":[142],"And":[143],"average":[145,201],"rate":[147],"reach":[149],"94":[150],"percent,":[151,213],"much":[152],"better":[153],"result":[158],"(less":[159],"65":[161],"percent).":[162],"further":[166],"OU-ISIR":[170,181],"test":[174],"its":[175],"generalization":[176],"ability":[177],"larger":[179],"data.":[180],"currently":[183],"largest":[185],"available":[187],"literature":[190],"recognition,":[193],"4,007":[195],"subjects.":[196],"On":[197],"dataset,":[199,235],"accuracy":[202],"under":[206],"identical":[207],"view":[208],"conditions":[209],"above":[211,221],"98":[212],"one":[216],"scenarios":[219],"91":[222],"percent.":[223],"Finally,":[224],"also":[227],"performs":[228],"USF":[233],"whose":[236],"sequences":[238],"are":[239],"imaged":[240],"real":[243],"outdoor":[244],"scene.":[245],"These":[246],"great":[249],"potential":[250],"practical":[255],"applications.":[256]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":61},{"year":2024,"cited_by_count":82},{"year":2023,"cited_by_count":103},{"year":2022,"cited_by_count":110},{"year":2021,"cited_by_count":116},{"year":2020,"cited_by_count":91},{"year":2019,"cited_by_count":83},{"year":2018,"cited_by_count":49},{"year":2017,"cited_by_count":29},{"year":2015,"cited_by_count":1}],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2025-10-10T00:00:00"}