{"id":"https://openalex.org/W2601916599","doi":"https://doi.org/10.1145/3019943.3020000","title":"Using Multi-Stage Features in Fast R-CNN for Pedestrian Detection","display_name":"Using Multi-Stage Features in Fast R-CNN for Pedestrian Detection","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2601916599","doi":"https://doi.org/10.1145/3019943.3020000","mag":"2601916599"},"language":"en","primary_location":{"id":"doi:10.1145/3019943.3020000","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3019943.3020000","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion","raw_type":"proceedings-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/A5051345429","display_name":"Miguel Farrajota","orcid":"https://orcid.org/0000-0001-7970-4649"},"institutions":[{"id":"https://openalex.org/I71503853","display_name":"University of Algarve","ror":"https://ror.org/014g34x36","country_code":"PT","type":"education","lineage":["https://openalex.org/I71503853"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"M. Farrajota","raw_affiliation_strings":["Vision Laboratory, LARSyS University of the Algarve, Faro, Portugal"],"affiliations":[{"raw_affiliation_string":"Vision Laboratory, LARSyS University of the Algarve, Faro, Portugal","institution_ids":["https://openalex.org/I71503853"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071191321","display_name":"Jo\u00e3o M. F. Rodrigues","orcid":"https://orcid.org/0000-0002-3562-6025"},"institutions":[{"id":"https://openalex.org/I71503853","display_name":"University of Algarve","ror":"https://ror.org/014g34x36","country_code":"PT","type":"education","lineage":["https://openalex.org/I71503853"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"J. M.F. Rodrigues","raw_affiliation_strings":["Vision Laboratory, LARSyS University of the Algarve, Faro, Portugal"],"affiliations":[{"raw_affiliation_string":"Vision Laboratory, LARSyS University of the Algarve, Faro, Portugal","institution_ids":["https://openalex.org/I71503853"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073695921","display_name":"J. M. H. du Buf","orcid":"https://orcid.org/0000-0002-4345-1237"},"institutions":[{"id":"https://openalex.org/I71503853","display_name":"University of Algarve","ror":"https://ror.org/014g34x36","country_code":"PT","type":"education","lineage":["https://openalex.org/I71503853"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"J. M.H. du Buf","raw_affiliation_strings":["Vision Laboratory, LARSyS University of the Algarve, Faro, Portugal"],"affiliations":[{"raw_affiliation_string":"Vision Laboratory, LARSyS University of the Algarve, Faro, Portugal","institution_ids":["https://openalex.org/I71503853"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5051345429"],"corresponding_institution_ids":["https://openalex.org/I71503853"],"apc_list":null,"apc_paid":null,"fwci":0.501,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.74868679,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"400","last_page":"407"},"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":0.9998000264167786,"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":0.9941999912261963,"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/pedestrian-detection","display_name":"Pedestrian detection","score":0.8379719257354736},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8215512037277222},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8212421536445618},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7525173425674438},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6804526448249817},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6324131488800049},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4971447288990021},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.49460121989250183},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49125975370407104},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.49109625816345215},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42079296708106995},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3474039137363434},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07712867856025696}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8379719257354736},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8215512037277222},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8212421536445618},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7525173425674438},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6804526448249817},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6324131488800049},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4971447288990021},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.49460121989250183},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49125975370407104},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49109625816345215},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42079296708106995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3474039137363434},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07712867856025696},{"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:10.1145/3019943.3020000","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3019943.3020000","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W82130502","https://openalex.org/W345900524","https://openalex.org/W753012316","https://openalex.org/W1475617732","https://openalex.org/W1487583988","https://openalex.org/W1536680647","https://openalex.org/W1556258326","https://openalex.org/W1686810756","https://openalex.org/W1903127635","https://openalex.org/W1976818984","https://openalex.org/W1986905809","https://openalex.org/W1992825118","https://openalex.org/W1999853363","https://openalex.org/W2031454541","https://openalex.org/W2074777933","https://openalex.org/W2097117768","https://openalex.org/W2098064689","https://openalex.org/W2117539524","https://openalex.org/W2125556102","https://openalex.org/W2151103935","https://openalex.org/W2155893237","https://openalex.org/W2156539399","https://openalex.org/W2156547346","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2162741153","https://openalex.org/W2163808566","https://openalex.org/W2170101770","https://openalex.org/W2194775991","https://openalex.org/W2200528286","https://openalex.org/W2314557688","https://openalex.org/W2499712614","https://openalex.org/W2548197316","https://openalex.org/W2949117887","https://openalex.org/W2949847849","https://openalex.org/W3097096317"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2751005898","https://openalex.org/W2802018156","https://openalex.org/W4313315626","https://openalex.org/W2101531944","https://openalex.org/W2913302899","https://openalex.org/W2922437833","https://openalex.org/W2100052226","https://openalex.org/W4312696271"],"abstract_inverted_index":{"Pedestrian":[0,154],"detection":[1,37,115,150],"and":[2,17,26,56,60,160],"tracking":[3],"remains":[4],"popular":[5],"issue":[6],"in":[7,13,38,90],"computer":[8],"vision,":[9],"with":[10,23,97,105],"many":[11],"applications":[12],"robotics,":[14],"surveillance,":[15],"security":[16],"telecare":[18],"systems,":[19],"especially":[20],"when":[21],"connected":[22],"Smart":[24,27],"Cities":[25],"Destinations.":[28],"As":[29],"a":[30,41,46,91],"particular":[31],"case":[32],"of":[33,49,100,135,142],"object":[34,114],"detection,":[35],"pedestrian":[36,123],"general":[39],"is":[40],"difficult":[42],"task":[43],"due":[44,51,67],"to":[45,52,65,68,74,80,146,158],"large":[47],"variability":[48],"features":[50,71,86,104,121,138],"different":[53,88,140],"scales,":[54],"views":[55],"occlusion.":[57],"Typically,":[58],"smaller":[59],"occluded":[61],"pedestrians":[62],"are":[63],"hard":[64],"detect":[66],"fewer":[69],"discriminative":[70],"if":[72],"compared":[73],"large-size,":[75],"visible":[76],"pedestrians.":[77],"In":[78,108],"order":[79],"overcome":[81],"this,":[82],"we":[83,111],"use":[84],"convolutional":[85,120,137],"from":[87,139],"stages":[89,141],"deep":[92],"Convolutional":[93],"Neural":[94],"Network":[95],"(CNN),":[96],"the":[98,128,133,143,148,162],"idea":[99],"combining":[101],"more":[102],"global":[103],"finer":[106],"details.":[107],"this":[109],"paper":[110],"present":[112],"an":[113],"framework":[116,126,131],"based":[117],"on":[118],"multi-stage":[119],"for":[122,132],"detection.":[124],"This":[125],"extends":[127],"Fast":[129],"R-CNN":[130],"combination":[134],"several":[136],"used":[144,157],"CNN":[145],"improve":[147],"network's":[149],"accuracy.":[151],"The":[152],"Caltech":[153],"dataset":[155],"was":[156],"train":[159],"evaluate":[161],"proposed":[163],"method.":[164]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}