{"id":"https://openalex.org/W4376852226","doi":"https://doi.org/10.1145/3573942.3574085","title":"Cross-View Gait Recognition Based on ViT and Convolution","display_name":"Cross-View Gait Recognition Based on ViT and Convolution","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4376852226","doi":"https://doi.org/10.1145/3573942.3574085"},"language":"en","primary_location":{"id":"doi:10.1145/3573942.3574085","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1145/3573942.3574085","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","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/A5030792628","display_name":"Xiaoyan Xie","orcid":"https://orcid.org/0000-0003-3544-2776"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoyan Xie","raw_affiliation_strings":["Xi'an University of Posts &amp; Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0003-3544-2776","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts &amp; Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035616201","display_name":"Zhaozhe Zhang","orcid":"https://orcid.org/0000-0003-1895-8025"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaozhe Zhang","raw_affiliation_strings":["Xi'an University of Posts &amp; Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0003-1895-8025","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts &amp; Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5030792628"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16090129,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":null,"first_page":"717","last_page":"722"},"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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":0.9882000088691711,"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/gait","display_name":"Gait","score":0.7945181131362915},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.725723385810852},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.6594303846359253},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6568392515182495},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6520060300827026},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.6336396336555481},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6040488481521606},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5137825608253479},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5045660734176636},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5025897026062012},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.4474225640296936},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23302319645881653},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.11723139882087708},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0700472891330719}],"concepts":[{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.7945181131362915},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.725723385810852},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6594303846359253},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6568392515182495},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6520060300827026},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.6336396336555481},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6040488481521606},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5137825608253479},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5045660734176636},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5025897026062012},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.4474225640296936},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23302319645881653},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.11723139882087708},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0700472891330719},{"id":"https://openalex.org/C42407357","wikidata":"https://www.wikidata.org/wiki/Q521","display_name":"Physiology","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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3573942.3574085","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1145/3573942.3574085","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2059901520","https://openalex.org/W2068715223","https://openalex.org/W2104335344","https://openalex.org/W2183341477","https://openalex.org/W2510190030","https://openalex.org/W2739325416","https://openalex.org/W2897874865","https://openalex.org/W2963180826","https://openalex.org/W2963301258","https://openalex.org/W3015249368","https://openalex.org/W3035400973","https://openalex.org/W3094502228","https://openalex.org/W3099206234","https://openalex.org/W3195830874","https://openalex.org/W4287022992","https://openalex.org/W6802024221"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2022849497","https://openalex.org/W3081299480","https://openalex.org/W2407190427","https://openalex.org/W2907584218","https://openalex.org/W2919210741","https://openalex.org/W3002446410","https://openalex.org/W4390224712","https://openalex.org/W4322096758","https://openalex.org/W2797752778"],"abstract_inverted_index":{"The":[0,90],"gait":[1,33,47,62,99],"recognition":[2,34],"draws":[3],"a":[4,31,53,68,110,115],"powerful":[5],"magnetizing":[6],"effect":[7],"on":[8,64,130],"biometric.":[9],"It":[10,151],"is":[11,71,128,152],"easily":[12],"affected":[13],"by":[14,37,114],"multiple":[15],"covariant":[16],"factors,":[17],"especially":[18],"clothing":[19],"occlusion":[20,86],"and":[21,40,56,87,95,123],"view":[22,88],"changing.":[23],"To":[24],"address":[25],"such":[26],"impact,":[27],"this":[28],"paper":[29],"proposes":[30],"cross-view":[32,155],"hybrid":[35],"framework,":[36],"integrate":[38],"convolution":[39,55],"ViT":[41,69],"into":[42],"discriminative":[43],"method.":[44],"Taking":[45],"the":[46,50,81,121,131,138],"silhouettes":[48,100],"as":[49],"original":[51],"input,":[52],"multi-layer":[54],"pooling":[57],"are":[58,101,107],"used":[59],"to":[60,73,79,109,119],"training":[61],"features":[63,75,97,106],"different":[65,77],"scales.":[66],"Then,":[67,104],"module":[70],"introduced":[72],"collect":[74],"from":[76],"angle,":[78],"reduce":[80],"influence":[82],"of":[83,85,93,98,144],"covariance":[84],"changes.":[89],"local":[91],"details":[92],"pixels":[94],"global":[96],"all":[102],"concerned.":[103],"two":[105],"fused":[108],"horizontal":[111],"pyramid,":[112],"trained":[113],"joint":[116],"loss":[117],"function,":[118],"enhance":[120],"discrimination":[122],"learning":[124],"ability.":[125],"Finally,":[126],"evaluation":[127],"performed":[129],"public":[132],"dataset":[133],"CASIA-B.":[134],"Experiments":[135],"show":[136],"that":[137],"proposed":[139],"method":[140],"achieves":[141],"average":[142],"accuracy":[143,156],"95.1%,":[145],"90.5%,":[146],"72.6%":[147],"in":[148,154],"three":[149],"states.":[150],"better":[153],"compared":[157],"with":[158],"current":[159],"methods.":[160]},"counts_by_year":[],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}
