{"id":"https://openalex.org/W4321021961","doi":"https://doi.org/10.1109/tcsvt.2023.3245613","title":"CFRLA-Net: A Context-Aware Feature Representation Learning Anchor-Free Network for Pedestrian Detection","display_name":"CFRLA-Net: A Context-Aware Feature Representation Learning Anchor-Free Network for Pedestrian Detection","publication_year":2023,"publication_date":"2023-02-16","ids":{"openalex":"https://openalex.org/W4321021961","doi":"https://doi.org/10.1109/tcsvt.2023.3245613"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2023.3245613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2023.3245613","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"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 Circuits and Systems for Video Technology","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/A5086468661","display_name":"Jun Li","orcid":"https://orcid.org/0000-0001-6509-1300"},"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/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]},{"id":"https://openalex.org/I4210151023","display_name":"Quanzhou Institute of Equipment Manufacturing Haixi Institute","ror":"https://ror.org/05ev3vr27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210151023"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Li","raw_affiliation_strings":["Department of Advanced Manufacturing, Fuzhou University, Quanzhou, China","Laboratory of Robotics and Intelligent Systems, Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou, China"],"affiliations":[{"raw_affiliation_string":"Department of Advanced Manufacturing, Fuzhou University, Quanzhou, China","institution_ids":["https://openalex.org/I80947539"]},{"raw_affiliation_string":"Laboratory of Robotics and Intelligent Systems, Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou, China","institution_ids":["https://openalex.org/I4210151023","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088039767","display_name":"Yuquan Bi","orcid":"https://orcid.org/0000-0003-4199-5596"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuquan Bi","raw_affiliation_strings":["Department of Advanced Manufacturing, Fuzhou University, Quanzhou, China"],"affiliations":[{"raw_affiliation_string":"Department of Advanced Manufacturing, Fuzhou University, Quanzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100714717","display_name":"Su-Mei Wang","orcid":"https://orcid.org/0000-0002-1285-1553"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Sumei Wang","raw_affiliation_strings":["Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026324175","display_name":"Qiming Li","orcid":"https://orcid.org/0000-0001-8317-5700"},"institutions":[{"id":"https://openalex.org/I4210151023","display_name":"Quanzhou Institute of Equipment Manufacturing Haixi Institute","ror":"https://ror.org/05ev3vr27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210151023"]},{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]},{"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":"Qiming Li","raw_affiliation_strings":["Department of Advanced Manufacturing, Fuzhou University, Quanzhou, China","Laboratory of Robotics and Intelligent Systems, Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou, China"],"affiliations":[{"raw_affiliation_string":"Department of Advanced Manufacturing, Fuzhou University, Quanzhou, China","institution_ids":["https://openalex.org/I80947539"]},{"raw_affiliation_string":"Laboratory of Robotics and Intelligent Systems, Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou, China","institution_ids":["https://openalex.org/I4210151023","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5086468661"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210151023","https://openalex.org/I80947539"],"apc_list":null,"apc_paid":null,"fwci":1.4738,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.8391056,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"33","issue":"9","first_page":"4948","last_page":"4961"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9993000030517578,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/computer-science","display_name":"Computer science","score":0.833722710609436},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.6951743364334106},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6510112881660461},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.636244535446167},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.628990888595581},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6279923915863037},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5392443537712097},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5114251971244812},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4482569098472595},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.44146254658699036},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.41113418340682983},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.2712513506412506},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1999817192554474},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08523586392402649}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.833722710609436},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.6951743364334106},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6510112881660461},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.636244535446167},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.628990888595581},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6279923915863037},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5392443537712097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5114251971244812},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4482569098472595},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.44146254658699036},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.41113418340682983},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.2712513506412506},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1999817192554474},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08523586392402649},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2023.3245613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2023.3245613","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"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 Circuits and Systems for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G4712084630","display_name":null,"funder_award_id":"2020C052","funder_id":"https://openalex.org/F4320336651","funder_display_name":"Quanzhou City Science and Technology Program"},{"id":"https://openalex.org/G7440858374","display_name":null,"funder_award_id":"2020J05083","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"},{"id":"https://openalex.org/G913817173","display_name":null,"funder_award_id":"62102394","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/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null},{"id":"https://openalex.org/F4320336651","display_name":"Quanzhou City Science and Technology Program","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":92,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1908020446","https://openalex.org/W2031454541","https://openalex.org/W2108598243","https://openalex.org/W2125215748","https://openalex.org/W2141231702","https://openalex.org/W2141364309","https://openalex.org/W2164475608","https://openalex.org/W2200528286","https://openalex.org/W2428342532","https://openalex.org/W2490270993","https://openalex.org/W2497039038","https://openalex.org/W2547288200","https://openalex.org/W2565639579","https://openalex.org/W2589858586","https://openalex.org/W2594507094","https://openalex.org/W2609144136","https://openalex.org/W2612624696","https://openalex.org/W2613599172","https://openalex.org/W2775890136","https://openalex.org/W2792824754","https://openalex.org/W2798542761","https://openalex.org/W2799199435","https://openalex.org/W2803973142","https://openalex.org/W2807183728","https://openalex.org/W2883363148","https://openalex.org/W2884561390","https://openalex.org/W2894820835","https://openalex.org/W2895451584","https://openalex.org/W2895675617","https://openalex.org/W2896540732","https://openalex.org/W2902502320","https://openalex.org/W2908510526","https://openalex.org/W2916798096","https://openalex.org/W2941356874","https://openalex.org/W2959863374","https://openalex.org/W2962850098","https://openalex.org/W2963037989","https://openalex.org/W2963093690","https://openalex.org/W2963315052","https://openalex.org/W2963318220","https://openalex.org/W2963404857","https://openalex.org/W2963604034","https://openalex.org/W2963681621","https://openalex.org/W2963769056","https://openalex.org/W2963844898","https://openalex.org/W2963925437","https://openalex.org/W2963927307","https://openalex.org/W2964080601","https://openalex.org/W2964121718","https://openalex.org/W2982466332","https://openalex.org/W2982619380","https://openalex.org/W2986357608","https://openalex.org/W2989604896","https://openalex.org/W2990075400","https://openalex.org/W2990130718","https://openalex.org/W2996839727","https://openalex.org/W2997344726","https://openalex.org/W3000240650","https://openalex.org/W3007529723","https://openalex.org/W3012573144","https://openalex.org/W3033116542","https://openalex.org/W3034638324","https://openalex.org/W3034955056","https://openalex.org/W3092665420","https://openalex.org/W3092687228","https://openalex.org/W3093583077","https://openalex.org/W3094213821","https://openalex.org/W3095638603","https://openalex.org/W3106250896","https://openalex.org/W3130394099","https://openalex.org/W3131500599","https://openalex.org/W3138516171","https://openalex.org/W3164282918","https://openalex.org/W3172509117","https://openalex.org/W3173632533","https://openalex.org/W3177452291","https://openalex.org/W4225829036","https://openalex.org/W4226224676","https://openalex.org/W4285229583","https://openalex.org/W4291014978","https://openalex.org/W6729242280","https://openalex.org/W6750697433","https://openalex.org/W6752196072","https://openalex.org/W6757817989","https://openalex.org/W6760424586","https://openalex.org/W6769628381","https://openalex.org/W6774527064","https://openalex.org/W6784464299","https://openalex.org/W6785652829","https://openalex.org/W6804570246","https://openalex.org/W6910687937"],"related_works":["https://openalex.org/W2624903463","https://openalex.org/W3207051105","https://openalex.org/W2093384100","https://openalex.org/W4386960967","https://openalex.org/W4313452767","https://openalex.org/W2083896825","https://openalex.org/W2802018156","https://openalex.org/W2913302899","https://openalex.org/W3204901196","https://openalex.org/W4309346246"],"abstract_inverted_index":{"High":[0],"resolution":[1],"and":[2,42,57,83,90,106,125,192,228],"strong":[3],"semantic":[4,56,120],"representation":[5],"are":[6],"both":[7],"vital":[8],"for":[9,26,39,81,213],"feature":[10,208],"extraction":[11,209],"networks":[12],"of":[13,136,188,205,211],"pedestrian":[14,27,141,194],"detection.":[15,28],"The":[16,96,180],"existing":[17],"high-resolution":[18,140],"network":[19],"(HRNet)":[20],"has":[21,35],"presented":[22],"a":[23,65,74,100,145,185],"promising":[24],"performance":[25,224],"However,":[29],"we":[30,48,63,143],"observed":[31],"that":[32,220],"it":[33],"still":[34],"some":[36],"significant":[37],"shortcomings":[38,53],"heavily":[40,190],"occluded":[41,191],"small-scale":[43,193],"pedestrians.":[44],"In":[45],"this":[46],"paper,":[47],"propose":[49,64,144],"to":[50,103,132],"address":[51],"the":[52,118,134,159,163,167,189,203,206,214],"by":[54,122,128],"extracting":[55],"spatial":[58],"context":[59,121,127],"from":[60],"HRNet.":[61],"Specifically,":[62],"Context-aware":[66,173],"Feature":[67,76,174],"Representation":[68,175],"Learning":[69,176],"Module":[70],"(CFRL-Module),":[71],"which":[72,115,150,199],"combines":[73],"Multi-scale":[75],"Context":[77],"Extraction":[78],"Parallel":[79],"Block":[80],"Convolution":[82],"Self-attention":[84],"(CEPCA-Block)":[85],"with":[86,110,166],"two":[87],"parallel":[88,101],"paths":[89],"an":[91],"Equivalent":[92],"FFN":[93],"(EFFN)":[94],"Block.":[95],"core":[97],"CEPCA-Block":[98],"adopts":[99],"design":[102],"integrate":[104],"convolution":[105,123],"multi-head":[107],"self-attention":[108],"(MHSA)":[109],"low":[111],"parameter":[112],"computational":[113],"cost,":[114],"can":[116,151,183,200],"obtain":[117],"deep":[119],"path":[124],"precise":[126],"MHSA":[129,138],"path.":[130],"Furthermore,":[131],"overcome":[133],"inefficiency":[135],"global":[137],"in":[139],"detection,":[142],"novel":[146],"local":[147],"window":[148],"MHSA,":[149],"significantly":[152],"reduce":[153],"memory":[154],"consumption":[155],"but":[156],"barely":[157],"affect":[158],"detection":[160,169],"performance.":[161],"Cascading":[162],"proposed":[164,181],"CFRL-Module":[165],"anchor-free":[168],"head":[170],"constitutes":[171],"our":[172],"Anchor-Free":[177],"Network":[178],"(CFRLA-Net).":[179],"CFRLA-Net":[182,221],"catch":[184],"high-level":[186],"understanding":[187],"instances":[195],"based":[196],"on":[197,225],"HRNet,":[198],"effectively":[201],"solve":[202],"limitation":[204],"insufficient":[207],"ability":[210],"HRNet":[212],"hard":[215],"samples.":[216],"Experimental":[217],"results":[218],"show":[219],"achieves":[222],"state-of-the-art":[223],"CityPersons,":[226],"Caltech,":[227],"CrowdHuman":[229],"benchmarks.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}