{"id":"https://openalex.org/W4406890482","doi":"https://doi.org/10.1109/access.2025.3535702","title":"Research on Pedestrian Re-Identification Based on Enhanced Residual Attention Network","display_name":"Research on Pedestrian Re-Identification Based on Enhanced Residual Attention Network","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4406890482","doi":"https://doi.org/10.1109/access.2025.3535702"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3535702","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3535702","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3535702","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087324161","display_name":"Liqin Liu","orcid":"https://orcid.org/0000-0003-0563-8559"},"institutions":[{"id":"https://openalex.org/I4210090490","display_name":"National University","ror":"https://ror.org/000a8qk84","country_code":"PH","type":"education","lineage":["https://openalex.org/I4210090490"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Liqin Liu","raw_affiliation_strings":["College of Computing and Information Technologies, National University, Manila, Philippines"],"raw_orcid":"https://orcid.org/0000-0003-0563-8559","affiliations":[{"raw_affiliation_string":"College of Computing and Information Technologies, National University, Manila, Philippines","institution_ids":["https://openalex.org/I4210090490"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027459129","display_name":"Eric Blancaflor","orcid":"https://orcid.org/0000-0002-7189-3040"},"institutions":[{"id":"https://openalex.org/I137967721","display_name":"Map\u00faa University","ror":"https://ror.org/040rd2b57","country_code":"PH","type":"facility","lineage":["https://openalex.org/I137967721"]},{"id":"https://openalex.org/I5791819","display_name":"University of the Philippines Manila","ror":"https://ror.org/01rrczv41","country_code":"PH","type":"education","lineage":["https://openalex.org/I103911934","https://openalex.org/I5791819"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Eric Blancaflor","raw_affiliation_strings":["School of Information Technology, Map&#x00FA;a University, Manila, Philippines","School of Information Technology, Mapua University, Manila, Philippines"],"raw_orcid":"https://orcid.org/0000-0002-7189-3040","affiliations":[{"raw_affiliation_string":"School of Information Technology, Map&#x00FA;a University, Manila, Philippines","institution_ids":["https://openalex.org/I5791819"]},{"raw_affiliation_string":"School of Information Technology, Mapua University, Manila, Philippines","institution_ids":["https://openalex.org/I137967721"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005359226","display_name":"Mideth Abisado","orcid":"https://orcid.org/0000-0003-4215-7260"},"institutions":[{"id":"https://openalex.org/I4210090490","display_name":"National University","ror":"https://ror.org/000a8qk84","country_code":"PH","type":"education","lineage":["https://openalex.org/I4210090490"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Mideth Abisado","raw_affiliation_strings":["College of Computing and Information Technologies, National University, Manila, Philippines"],"raw_orcid":"https://orcid.org/0000-0003-4215-7260","affiliations":[{"raw_affiliation_string":"College of Computing and Information Technologies, National University, Manila, Philippines","institution_ids":["https://openalex.org/I4210090490"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.7655,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66655897,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"13","issue":null,"first_page":"27587","last_page":"27595"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9444000124931335,"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":0.9444000124931335,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9241999983787537,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.7304885387420654},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6803551912307739},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6684557795524597},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.6240968704223633},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.42364710569381714},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40360841155052185},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1764516830444336},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.11518850922584534},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11218604445457458}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7304885387420654},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6803551912307739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6684557795524597},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6240968704223633},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.42364710569381714},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40360841155052185},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1764516830444336},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.11518850922584534},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11218604445457458},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3535702","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3535702","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:527e0181312c45b6ab38a30b5af8e29c","is_oa":true,"landing_page_url":"https://doaj.org/article/527e0181312c45b6ab38a30b5af8e29c","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 27587-27595 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3535702","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3535702","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2467139031","https://openalex.org/W2502225121","https://openalex.org/W2598634450","https://openalex.org/W2606377603","https://openalex.org/W2724213014","https://openalex.org/W2904427185","https://openalex.org/W2962706983","https://openalex.org/W2963000559","https://openalex.org/W2963322158","https://openalex.org/W2963383990","https://openalex.org/W2963637710","https://openalex.org/W2963910742","https://openalex.org/W3034372982","https://openalex.org/W3034727830","https://openalex.org/W3035070480","https://openalex.org/W3035373548","https://openalex.org/W3044253019","https://openalex.org/W3093078906","https://openalex.org/W3098711604","https://openalex.org/W3106772544","https://openalex.org/W3109976102","https://openalex.org/W3174940004","https://openalex.org/W4220813980","https://openalex.org/W4225264041","https://openalex.org/W4294068925","https://openalex.org/W4380303278","https://openalex.org/W4397026482","https://openalex.org/W4401512563","https://openalex.org/W6745280964","https://openalex.org/W6766768617","https://openalex.org/W6771395016"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W3122828758","https://openalex.org/W2101960027","https://openalex.org/W4205958986","https://openalex.org/W2197846993","https://openalex.org/W49697837","https://openalex.org/W2586575957","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"Pedestrian":[0],"re-identification":[1,182],"is":[2,61,160],"influenced":[3],"by":[4,48],"various":[5],"complex":[6],"factors":[7],"such":[8],"as":[9,81],"camera":[10],"angles,":[11],"posture":[12],"changes,":[13],"and":[14,44,64,102,148,176],"object":[15],"occlusion.":[16],"How":[17],"to":[18,162],"effectively":[19,62,86],"extract":[20,172],"discriminative":[21,72,173],"feature":[22,127],"information":[23],"remains":[24],"a":[25,82,143],"key":[26,59],"challenge.":[27],"To":[28],"address":[29],"this":[30,32],"issue,":[31],"paper":[33],"proposes":[34],"an":[35,149],"enhanced":[36],"neural":[37],"network":[38,118],"based":[39],"on":[40,92,155],"residual":[41,54],"attention":[42,50],"mechanism":[43,51],"random":[45,79],"erasure.":[46],"Firstly,":[47],"introducing":[49],"in":[52,125],"the":[53,56,71,75,88,93,98,104,117,126,139,156,167,178],"network,":[55],"response":[57],"of":[58,74,90,100,116,123,146,153,180],"features":[60,66,175],"strengthened":[63],"redundant":[65],"are":[67],"suppressed,":[68],"thereby":[69,129],"improving":[70,131],"performance":[73,122,179],"network.":[76],"Secondly,":[77],"using":[78,110],"erasure":[80],"data":[83],"augmentation":[84],"method":[85,141,169],"alleviates":[87],"impact":[89],"occlusion":[91],"model\u2019s":[94],"recognition":[95,132],"ability,":[96],"reduces":[97],"risk":[99],"overfitting,":[101],"improves":[103],"network\u2019s":[105],"generalization":[106],"performance.":[107],"In":[108],"addition,":[109],"triplet":[111],"loss":[112],"for":[113],"supervised":[114],"training":[115],"promotes":[119],"better":[120,171],"clustering":[121],"samples":[124],"space,":[128],"significantly":[130],"accuracy.":[133],"The":[134],"experimental":[135],"results":[136],"show":[137],"that":[138,166],"proposed":[140,168],"achieves":[142],"Rank-1":[144],"accuracy":[145,151],"88.55%":[147],"average":[150],"(mAP)":[152],"79.45%":[154],"Market-1501":[157],"dataset,":[158],"which":[159],"superior":[161],"existing":[163],"methods.":[164],"Verified":[165],"can":[170],"pedestrian":[174,181],"improve":[177],"models.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
