{"id":"https://openalex.org/W3024191569","doi":"https://doi.org/10.1109/lsp.2020.2995102","title":"Efficient Proposals: Scale Estimation for Object Proposals in Pedestrian Detection Tasks","display_name":"Efficient Proposals: Scale Estimation for Object Proposals in Pedestrian Detection Tasks","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3024191569","doi":"https://doi.org/10.1109/lsp.2020.2995102","mag":"3024191569"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2020.2995102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2020.2995102","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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/A5034220816","display_name":"Ji Qiu","orcid":"https://orcid.org/0000-0002-9880-479X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ji Qiu","raw_affiliation_strings":["Beijing Jiaotong University, Beijing"],"raw_orcid":"https://orcid.org/0000-0002-9880-479X","affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071348006","display_name":"Lide Wang","orcid":"https://orcid.org/0000-0002-0230-5321"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lide Wang","raw_affiliation_strings":["Beijing Jiaotong University, Beijing"],"raw_orcid":"https://orcid.org/0000-0002-0230-5321","affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100330959","display_name":"Yin Wang","orcid":"https://orcid.org/0000-0001-6804-1202"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yin Wang","raw_affiliation_strings":["Beijing Jiaotong University, Beijing"],"raw_orcid":"https://orcid.org/0000-0001-6804-1202","affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100721274","display_name":"Yu Hen Hu","orcid":"https://orcid.org/0000-0003-3427-0677"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Hen Hu","raw_affiliation_strings":["University of Wisconsin-Madison, Madison, WI, USA"],"raw_orcid":"https://orcid.org/0000-0003-3427-0677","affiliations":[{"raw_affiliation_string":"University of Wisconsin-Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2875,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.5449535,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"27","issue":null,"first_page":"855","last_page":"859"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9955000281333923,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.6866270303726196},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6160866618156433},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.5612122416496277},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5461349487304688},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.48790261149406433},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.47986119985580444},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47029587626457214},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4229488968849182},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36201298236846924},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3334953784942627},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3063768744468689},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12545165419578552},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10788989067077637},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.08772805333137512},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07886826992034912}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6866270303726196},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6160866618156433},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.5612122416496277},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5461349487304688},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.48790261149406433},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.47986119985580444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47029587626457214},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4229488968849182},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36201298236846924},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3334953784942627},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3063768744468689},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12545165419578552},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10788989067077637},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.08772805333137512},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07886826992034912},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2020.2995102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2020.2995102","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7699999809265137}],"awards":[{"id":"https://openalex.org/G3127491282","display_name":null,"funder_award_id":"L171009","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"}],"funders":[{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1958328135","https://openalex.org/W2031454541","https://openalex.org/W2087070363","https://openalex.org/W2155482699","https://openalex.org/W2236623899","https://openalex.org/W2569272946","https://openalex.org/W2570343428","https://openalex.org/W2757028014","https://openalex.org/W2765774298","https://openalex.org/W2791440580","https://openalex.org/W2796347433","https://openalex.org/W2884367402","https://openalex.org/W2888527098","https://openalex.org/W2912149700","https://openalex.org/W2963315052","https://openalex.org/W2963579094","https://openalex.org/W2970869172","https://openalex.org/W2972838552","https://openalex.org/W2995649684","https://openalex.org/W4293584584"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2802018156","https://openalex.org/W4313315626","https://openalex.org/W2101531944","https://openalex.org/W2922437833","https://openalex.org/W2100052226","https://openalex.org/W4312696271","https://openalex.org/W4223892596","https://openalex.org/W2933098581"],"abstract_inverted_index":{"Due":[0],"to":[1,44,58],"projective":[2],"transformation,":[3],"a":[4,54,89,111,115],"great":[5],"variety":[6],"of":[7,46,62,88],"pedestrian":[8],"sizes":[9,61],"appears":[10],"on":[11,15,66,69,110],"the":[12,19,27,60,70,86],"image":[13],"depending":[14],"their":[16,67],"depths":[17],"in":[18,32,39],"real":[20],"world.":[21],"In":[22],"this":[23,50],"letter,":[24],"we":[25,52],"analyze":[26],"object":[28,34,63,103],"proposal":[29,104],"generation":[30],"strategies":[31],"existing":[33],"detection":[35],"methods":[36,106],"that":[37],"underperform":[38],"some":[40],"real-world":[41],"applications":[42],"owing":[43],"ignorance":[45],"spatial":[47],"factors.":[48],"To":[49],"end,":[51],"propose":[53],"neural":[55],"network":[56],"predictor":[57],"estimate":[59],"proposals":[64,79],"based":[65],"locations":[68],"image.":[71],"Furthermore,":[72],"our":[73],"model":[74],"can":[75],"efficiently":[76],"generate":[77],"high-quality":[78],"using":[80],"very":[81],"few":[82],"training":[83],"samples":[84],"with":[85],"help":[87],"data":[90],"augmentation":[91],"strategy.":[92],"The":[93],"proposed":[94],"size":[95],"estimation":[96,105],"method":[97],"is":[98],"compared":[99],"against":[100],"several":[101],"state-of-art":[102],"by":[107],"two":[108],"metrics":[109],"driving":[112],"dataset":[113,119],"and":[114],"train":[116],"station":[117],"surveillance":[118],"which":[120],"shows":[121],"significant":[122],"performance":[123],"advantages.":[124]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
