{"id":"https://openalex.org/W3119042803","doi":"https://doi.org/10.1109/ijcb48548.2020.9304883","title":"DVRNet: Decoupled Visible Region Network for Pedestrian Detection","display_name":"DVRNet: Decoupled Visible Region Network for Pedestrian Detection","publication_year":2020,"publication_date":"2020-09-28","ids":{"openalex":"https://openalex.org/W3119042803","doi":"https://doi.org/10.1109/ijcb48548.2020.9304883","mag":"3119042803"},"language":"en","primary_location":{"id":"doi:10.1109/ijcb48548.2020.9304883","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb48548.2020.9304883","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","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/A5100658549","display_name":"Lei Shi","orcid":"https://orcid.org/0000-0002-9536-336X"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lei Shi","raw_affiliation_strings":["Department of Computer Science, Computational Biomedicine Lab, University of Houston, Texas, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Computational Biomedicine Lab, University of Houston, Texas, USA","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035191317","display_name":"Charles Livermore","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charles Livermore","raw_affiliation_strings":["Department of Computer Science, Computational Biomedicine Lab, University of Houston, Texas, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Computational Biomedicine Lab, University of Houston, Texas, USA","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101875396","display_name":"Ioannis A. Kakadiaris","orcid":"https://orcid.org/0000-0001-5983-7268"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ioannis A. Kakadiaris","raw_affiliation_strings":["Department of Computer Science, Computational Biomedicine Lab, University of Houston, Texas, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Computational Biomedicine Lab, University of Houston, Texas, USA","institution_ids":["https://openalex.org/I44461941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100658549"],"corresponding_institution_ids":["https://openalex.org/I44461941"],"apc_list":null,"apc_paid":null,"fwci":0.1954,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.52048464,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9987999796867371,"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":0.9987999796867371,"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":0.9959999918937683,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/discriminative-model","display_name":"Discriminative model","score":0.8185568451881409},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.7404875159263611},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7165435552597046},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.6787779331207275},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6621085405349731},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6160460710525513},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.58994060754776},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.5415170192718506},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.52191162109375},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.517507791519165},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.49915504455566406},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4557691216468811},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42457765340805054},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.422296404838562},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09433183073997498},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06910949945449829}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8185568451881409},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7404875159263611},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7165435552597046},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.6787779331207275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6621085405349731},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6160460710525513},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.58994060754776},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.5415170192718506},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.52191162109375},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.517507791519165},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.49915504455566406},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4557691216468811},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42457765340805054},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.422296404838562},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09433183073997498},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06910949945449829},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"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/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/ijcb48548.2020.9304883","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb48548.2020.9304883","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.75,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G8985480710","display_name":null,"funder_award_id":"2017-ST-BTI-0001-0201","funder_id":"https://openalex.org/F4320306110","funder_display_name":"U.S. Department of Homeland Security"}],"funders":[{"id":"https://openalex.org/F4320306110","display_name":"U.S. Department of Homeland Security","ror":"https://ror.org/00jyr0d86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1533861849","https://openalex.org/W1536680647","https://openalex.org/W2107775979","https://openalex.org/W2115669554","https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2565639579","https://openalex.org/W2594507094","https://openalex.org/W2613718673","https://openalex.org/W2792824754","https://openalex.org/W2798542761","https://openalex.org/W2799199435","https://openalex.org/W2883363148","https://openalex.org/W2888941747","https://openalex.org/W2894820835","https://openalex.org/W2895451584","https://openalex.org/W2896540732","https://openalex.org/W2913318911","https://openalex.org/W2963150697","https://openalex.org/W2963351448","https://openalex.org/W2963404857","https://openalex.org/W2963681621","https://openalex.org/W2963769056","https://openalex.org/W2983760502","https://openalex.org/W2990075400","https://openalex.org/W6620707391","https://openalex.org/W6631943919","https://openalex.org/W6750697433"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W2153315159","https://openalex.org/W2608226141","https://openalex.org/W2751005898","https://openalex.org/W4385725200","https://openalex.org/W3208297503","https://openalex.org/W3119773509","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W2619127353"],"abstract_inverted_index":{"Pedestrian":[0],"detection":[1,31],"remains":[2],"a":[3,41,50,64],"challenging":[4],"task":[5],"due":[6],"to":[7,25,32,123],"the":[8,27,34,71,88,94,101,116,119,130,134,143,149,152],"problems":[9],"caused":[10],"by":[11,83,115],"occlusion":[12],"variance.":[13],"Visible-body":[14],"bounding":[15],"boxes":[16],"are":[17],"typically":[18],"used":[19],"as":[20],"an":[21],"extra":[22],"supervision":[23,121],"signal":[24],"improve":[26],"performance":[28],"of":[29,44,52,129,136],"pedestrian":[30,75],"predict":[33],"full-body.":[35],"However,":[36],"visible-body":[37,73,117],"assisted":[38,74],"approaches":[39],"produce":[40],"large":[42],"number":[43],"false":[45],"positives,":[46],"which":[47,107],"result":[48],"from":[49],"lack":[51],"adequate":[53],"and":[54,100,118,132,151],"discriminative":[55,126],"full-body":[56,131],"contextual":[57,127],"information.":[58],"In":[59],"this":[60],"paper,":[61],"we":[62,80],"propose":[63],"new":[65],"network,":[66],"dubbed":[67],"DVRNet,":[68],"based":[69],"on":[70,148],"representative":[72],"detector":[76],"named":[77,87],"Bi-box.":[78],"Specifically,":[79],"extend":[81],"Bi-box":[82],"adding":[84],"three":[85],"modules":[86],"attention-based":[89],"feature":[90,103,137],"interleaver":[91],"module":[92,98,105],"(AFIM),":[93],"binary":[95],"mask":[96],"learning":[97],"(BMLM),":[99],"head-aware":[102],"enhancement":[104],"(HFEM),":[106],"play":[108],"important":[109],"roles":[110],"in":[111],"employing":[112],"features":[113],"learned":[114],"head":[120],"signals":[122],"enrich":[124],"high":[125],"information":[128],"enhance":[133],"power":[135],"representation.":[138],"Experimental":[139],"results":[140,147],"indicate":[141],"that":[142],"DVRNet":[144],"achieves":[145],"promising":[146],"CityPersons":[150],"CrowdHuman":[153],"datasets.":[154]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
