{"id":"https://openalex.org/W4316021904","doi":"https://doi.org/10.1109/tim.2022.3230459","title":"A Smart Metro Passenger Detector Based on Two Mode MetroNexts","display_name":"A Smart Metro Passenger Detector Based on Two Mode MetroNexts","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4316021904","doi":"https://doi.org/10.1109/tim.2022.3230459"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2022.3230459","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tim.2022.3230459","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Instrumentation and Measurement","raw_type":"journal-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/A5058600941","display_name":"Qiang Guo","orcid":"https://orcid.org/0000-0002-6038-7361"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiang Guo","raw_affiliation_strings":["School of Control Science and Engineering, Dalian University of Technology, Dalian, China","Key Laboratory of Intelligent Control and Optimization for Industrial Equipment (Dalian University of Technology), Ministry of Education, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Control Science and Engineering, Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]},{"raw_affiliation_string":"Key Laboratory of Intelligent Control and Optimization for Industrial Equipment (Dalian University of Technology), Ministry of Education, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042870470","display_name":"Quanli Liu","orcid":"https://orcid.org/0000-0003-4118-2368"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quanli Liu","raw_affiliation_strings":["School of Control Science and Engineering, Dalian University of Technology, Dalian, China","Key Laboratory of Intelligent Control and Optimization for Industrial Equipment (Dalian University of Technology), Ministry of Education, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Control Science and Engineering, Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]},{"raw_affiliation_string":"Key Laboratory of Intelligent Control and Optimization for Industrial Equipment (Dalian University of Technology), Ministry of Education, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103147232","display_name":"Yuanqing Zhang","orcid":"https://orcid.org/0000-0001-5632-9639"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuanqing Zhang","raw_affiliation_strings":["Dalian Seasky Automation Company, Ltd., Dalian, China"],"affiliations":[{"raw_affiliation_string":"Dalian Seasky Automation Company, Ltd., Dalian, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101748721","display_name":"Qiang Kang","orcid":"https://orcid.org/0000-0001-5922-4971"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiang Kang","raw_affiliation_strings":["Dalian Seasky Automation Company, Ltd., Dalian, China"],"affiliations":[{"raw_affiliation_string":"Dalian Seasky Automation Company, Ltd., Dalian, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5058600941"],"corresponding_institution_ids":["https://openalex.org/I27357992"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00355235,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9991000294685364,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9991000294685364,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9984999895095825,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/benchmark","display_name":"Benchmark (surveying)","score":0.7011856436729431},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6941779255867004},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6558672189712524},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6438713669776917},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.6226390600204468},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.5611506700515747},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5010199546813965},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.45977649092674255},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.45770952105522156},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40437835454940796},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.35030287504196167},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2631041407585144},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1431387960910797},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.13089075684547424}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7011856436729431},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6941779255867004},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6558672189712524},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6438713669776917},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.6226390600204468},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.5611506700515747},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5010199546813965},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.45977649092674255},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.45770952105522156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40437835454940796},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.35030287504196167},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2631041407585144},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1431387960910797},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.13089075684547424},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2022.3230459","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tim.2022.3230459","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G751850055","display_name":null,"funder_award_id":"61773085","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1650122911","https://openalex.org/W1986905809","https://openalex.org/W2031454541","https://openalex.org/W2036490799","https://openalex.org/W2104671481","https://openalex.org/W2125066085","https://openalex.org/W2156547346","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2291533986","https://openalex.org/W2411655674","https://openalex.org/W2497039038","https://openalex.org/W2523020334","https://openalex.org/W2534262995","https://openalex.org/W2548197316","https://openalex.org/W2565639579","https://openalex.org/W2594507094","https://openalex.org/W2624405149","https://openalex.org/W2741620214","https://openalex.org/W2782811019","https://openalex.org/W2792824754","https://openalex.org/W2894820835","https://openalex.org/W2899077285","https://openalex.org/W2933493913","https://openalex.org/W2950656546","https://openalex.org/W2956268511","https://openalex.org/W2963087201","https://openalex.org/W2963315052","https://openalex.org/W2963579094","https://openalex.org/W2963681621","https://openalex.org/W2963998989","https://openalex.org/W2964027659","https://openalex.org/W2964241181","https://openalex.org/W3010535780","https://openalex.org/W3025191651","https://openalex.org/W3034971973","https://openalex.org/W3089807207","https://openalex.org/W3090757175","https://openalex.org/W3098140381","https://openalex.org/W3106234331","https://openalex.org/W3109173645","https://openalex.org/W3110796402","https://openalex.org/W3138516171","https://openalex.org/W3151111735","https://openalex.org/W3173114505","https://openalex.org/W4226336530","https://openalex.org/W4283457846","https://openalex.org/W4293584584","https://openalex.org/W4312554764","https://openalex.org/W6684811926","https://openalex.org/W6730780348","https://openalex.org/W6749810415","https://openalex.org/W6750227808","https://openalex.org/W6787650128"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2378211422","https://openalex.org/W1557094818","https://openalex.org/W2101960027","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2972620127","https://openalex.org/W2608226141","https://openalex.org/W2981141433"],"abstract_inverted_index":{"Accurate":[0],"metro":[1,12,26,115,208,223,243,249],"passenger":[2,239],"detection":[3,135,146,151,161,179],"is":[4,17,129],"considered":[5],"a":[6,53,174,197,233],"fundamental":[7],"technique":[8],"for":[9,23,40,76,177],"the":[10,38,44,61,80,101,118,124,139,143,191,202,212,215,219],"smart":[11,234],"station.":[13],"The":[14],"major":[15],"challenge":[16],"false":[18],"positives":[19],"(FPs)":[20],"when":[21],"hunting":[22],"passengers":[24],"in":[25,43,196,204,222,245],"carriages":[27],"and":[28,51,67,73,92,97,113,131,157,167,201,207,247],"stations.":[29],"This":[30],"article":[31,86],"provides":[32],"an":[33],"in-depth":[34],"analysis":[35],"to":[36,59,99,187,236,242],"reveal":[37],"reasons":[39],"this":[41,85],"problem":[42],"two-stage":[45],"convolutional":[46],"neural":[47],"network":[48],"(CNN)-based":[49],"detector":[50],"proposes":[52],"novel":[54],"two-mode":[55,81,125,192],"refined":[56,82,126],"proposals":[57,127],"algorithm":[58,128],"address":[60],"problem,":[62],"which":[63],"includes":[64],"cascade":[65],"mode":[66],"intrinsic":[68],"information":[69,241],"mode,":[70],"or":[71],"CRP":[72],"IRP":[74],"algorithms":[75],"short.":[77],"Aided":[78],"by":[79,137],"Proposals":[83],"algorithm,":[84],"designs":[87],"two":[88,114,216],"small":[89],"but":[90],"fast":[91,164],"accurate":[93,238],"pedestrian":[94,134,178],"detectors:":[95],"MetroNext-CRP":[96,148],"MetroNext-IRP":[98,158],"meet":[100],"application":[102],"requirements":[103],"of":[104,214],"different":[105],"tasks.":[106],"Based":[107],"on":[108,181],"various":[109,182],"challenging":[110],"benchmark":[111],"datasets":[112],"scene":[116],"datasets,":[117],"experimental":[119],"results":[120,152],"have":[121],"demonstrated":[122],"that":[123,227],"effective":[130],"can":[132,229],"improve":[133],"accuracy":[136,162],"removing":[138],"FPs.":[140],"Compared":[141],"with":[142,153,163],"existing":[144],"state-of-the-art":[145],"networks,":[147],"achieves":[149],"competitive":[150],"acceptable":[154],"computational":[155],"cost":[156],"demonstrates":[159],"better":[160],"inference":[165],"speed":[166],"without":[168],"extra":[169],"memory":[170],"consumption,":[171],"thus":[172],"providing":[173],"practical":[175],"solution":[176],"tasks":[180],"hardware":[183],"platforms,":[184],"particularly":[185],"tailored":[186],"edge":[188],"devices.":[189],"Finally,":[190],"MetroNexts":[193],"are":[194],"deployed":[195],"metro\u2019s":[198],"onboard":[199],"computer,":[200],"experiments":[203,221],"real-life":[205],"campus":[206],"scenes":[209,224],"further":[210],"demonstrate":[211],"feasibility":[213],"detectors,":[217],"especially":[218],"field":[220],"strongly":[225],"support":[226],"they":[228],"be":[230],"served":[231],"as":[232],"instrument":[235],"provide":[237],"position":[240],"operators":[244],"complicated":[246],"realistic":[248],"scenes.":[250]},"counts_by_year":[],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}
