{"id":"https://openalex.org/W2939566963","doi":"https://doi.org/10.1109/fskd.2018.8686843","title":"A Stochastic Parallel Gradient Descent Algorithem for Pedestrian Re-identification","display_name":"A Stochastic Parallel Gradient Descent Algorithem for Pedestrian Re-identification","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2939566963","doi":"https://doi.org/10.1109/fskd.2018.8686843","mag":"2939566963"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2018.8686843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2018.8686843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101638925","display_name":"Keyang Cheng","orcid":"https://orcid.org/0000-0001-5240-1605"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keyang Cheng","raw_affiliation_strings":["Jiangsu University, School of Computer Science and Communication Engineering, Zhenjiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu University, School of Computer Science and Communication Engineering, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072705483","display_name":"Fei Tao","orcid":"https://orcid.org/0000-0002-9020-0633"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Tao","raw_affiliation_strings":["Jiangsu University, School of Computer Science and Communication Engineering, Zhenjiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu University, School of Computer Science and Communication Engineering, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100427565","display_name":"Jianming Zhang","orcid":"https://orcid.org/0000-0002-4278-0805"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianming Zhang","raw_affiliation_strings":["Jiangsu University, School of Computer Science and Communication Engineering, Zhenjiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu University, School of Computer Science and Communication Engineering, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I115592961"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2226613,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"221","last_page":"227"},"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":0.9988999962806702,"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":0.9983000159263611,"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/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.8784275650978088},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.777481198310852},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6942291855812073},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.636927604675293},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.633769154548645},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5893775820732117},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.5662509202957153},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5420021414756775},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.49563682079315186},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43323129415512085},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.389120489358902},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3756380081176758},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34550243616104126},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09517264366149902},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07295465469360352}],"concepts":[{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.8784275650978088},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.777481198310852},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6942291855812073},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.636927604675293},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.633769154548645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5893775820732117},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.5662509202957153},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5420021414756775},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.49563682079315186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43323129415512085},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.389120489358902},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3756380081176758},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34550243616104126},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09517264366149902},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07295465469360352},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2018.8686843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2018.8686843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.550000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W41482161","https://openalex.org/W91614106","https://openalex.org/W104184427","https://openalex.org/W140822990","https://openalex.org/W206659046","https://openalex.org/W825775831","https://openalex.org/W1522301498","https://openalex.org/W1967988963","https://openalex.org/W1973339529","https://openalex.org/W1979260620","https://openalex.org/W2005391548","https://openalex.org/W2007972795","https://openalex.org/W2046835352","https://openalex.org/W2047632871","https://openalex.org/W2176499795","https://openalex.org/W2221538265","https://openalex.org/W2294378923","https://openalex.org/W2294682666","https://openalex.org/W2295830131","https://openalex.org/W2298503502","https://openalex.org/W2396615952","https://openalex.org/W2403646140","https://openalex.org/W2473611539","https://openalex.org/W2510475319","https://openalex.org/W2518108298","https://openalex.org/W2526349169","https://openalex.org/W2564444812","https://openalex.org/W2586448151","https://openalex.org/W2714745381","https://openalex.org/W2770129579","https://openalex.org/W2772651650","https://openalex.org/W2949615858","https://openalex.org/W2964121744","https://openalex.org/W2964159641","https://openalex.org/W3102668440","https://openalex.org/W6631190155","https://openalex.org/W6642222135","https://openalex.org/W6652193465","https://openalex.org/W6733143881","https://openalex.org/W6740227565","https://openalex.org/W6746805552"],"related_works":["https://openalex.org/W1998698147","https://openalex.org/W4206903459","https://openalex.org/W2754816816","https://openalex.org/W4366280654","https://openalex.org/W3160167280","https://openalex.org/W4231621013","https://openalex.org/W4362706668","https://openalex.org/W3008318776","https://openalex.org/W1977633006","https://openalex.org/W1971945429"],"abstract_inverted_index":{"Pedestrians":[0],"re-identification":[1],"is":[2,26,83],"a":[3,17,60],"hot":[4],"topic":[5],"in":[6,21,100,122],"the":[7,31,36,42,47,78,94,113],"field":[8],"of":[9,30,38,44,93],"computer":[10],"vision.":[11],"Convolutional":[12],"neural":[13],"network(CNN)":[14],"has":[15],"achieved":[16],"good":[18],"recognition":[19],"effect":[20],"pedestrian":[22,33,75],"re-identification.":[23],"However,":[24],"CNN":[25,39],"computationally":[27],"intensive":[28],"because":[29],"vast":[32],"data":[34],"and":[35,52,90,119],"depth":[37],"training.":[40,101],"As":[41],"requirement":[43],"higher":[45],"accuracy,":[46],"training":[48,114],"always":[49],"takes":[50],"days":[51],"even":[53],"week.":[54],"In":[55],"this":[56,107],"paper,":[57],"we":[58],"proposed":[59],"parallel":[61,68],"stochastic":[62],"gradient":[63],"descent(SGD)":[64],"algorithm,":[65],"where":[66],"five-hierarchy":[67],"structure":[69],"sets":[70],"up":[71,112],"blocks":[72],"based":[73],"on":[74],"attributes.":[76],"Moreover,":[77],"interval":[79],"for":[80],"updating":[81],"parameters":[82],"analyzed":[84],"to":[85],"optimize":[86],"parameter":[87],"selections.":[88],"Momentum":[89],"adaptive":[91],"adjustment":[92],"learning":[95],"rate":[96],"are":[97],"also":[98],"adopted":[99],"Our":[102],"experiments":[103],"results":[104],"show":[105],"that":[106],"parallelization":[108],"method":[109],"successfully":[110],"speeds":[111],"process":[115],"by":[116],"five":[117],"times":[118],"surpasses":[120],"state-of-the-art":[121],"accuracy":[123],"as":[124],"well.":[125]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
