{"id":"https://openalex.org/W2974982301","doi":"https://doi.org/10.1109/tits.2019.2942045","title":"CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection","display_name":"CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection","publication_year":2019,"publication_date":"2019-09-25","ids":{"openalex":"https://openalex.org/W2974982301","doi":"https://doi.org/10.1109/tits.2019.2942045","mag":"2974982301"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2019.2942045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2019.2942045","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2212.05691","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100632663","display_name":"Tianliang Zhang","orcid":"https://orcid.org/0000-0002-3524-0878"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianliang Zhang","raw_affiliation_strings":["Beihang University, Beijing, China","School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3524-0878","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000034551","display_name":"Zhenjun Han","orcid":"https://orcid.org/0000-0002-9970-5152"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenjun Han","raw_affiliation_strings":["School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9970-5152","affiliations":[{"raw_affiliation_string":"School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101404512","display_name":"Huijuan Xu","orcid":"https://orcid.org/0000-0002-4778-5584"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huijuan Xu","raw_affiliation_strings":["University of California at Berkeley, Berkeley, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California at Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015525872","display_name":"Baochang Zhang","orcid":"https://orcid.org/0000-0001-7396-6218"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baochang Zhang","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7396-6218","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015317495","display_name":"Qixiang Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qixiang Ye","raw_affiliation_strings":["School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1215-6259","affiliations":[{"raw_affiliation_string":"School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6948,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.74947166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"21","issue":"11","first_page":"4593","last_page":"4604"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9986000061035156,"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":0.9965999722480774,"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/pedestrian-detection","display_name":"Pedestrian detection","score":0.8437793254852295},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7440371513366699},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7156165838241577},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6294641494750977},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5567083954811096},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5353598594665527},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5210280418395996},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4880343973636627},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.45036718249320984},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41246962547302246},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.403605192899704},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.20067301392555237}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8437793254852295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7440371513366699},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7156165838241577},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6294641494750977},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5567083954811096},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5353598594665527},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5210280418395996},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4880343973636627},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.45036718249320984},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41246962547302246},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.403605192899704},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.20067301392555237},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tits.2019.2942045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2019.2942045","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2212.05691","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.05691","pdf_url":"https://arxiv.org/pdf/2212.05691","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2212.05691","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.05691","pdf_url":"https://arxiv.org/pdf/2212.05691","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8199999928474426}],"awards":[{"id":"https://openalex.org/G1295000622","display_name":null,"funder_award_id":"61836012","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G262599272","display_name":"\u9ad8\u6e05\u6670\u5ea6\u822a\u62cd\u4e0e\u9065\u611f\u5f71\u50cf\u76ee\u6807\u9c81\u68d2\u6027\u68c0\u6d4b\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61771447","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5501667907","display_name":null,"funder_award_id":"Z181100008918014","funder_id":"https://openalex.org/F4320325902","funder_display_name":"Beijing Municipal Science and Technology Commission"},{"id":"https://openalex.org/G5939181645","display_name":"\u5f31\u76d1\u7763\u89c6\u89c9\u76ee\u6807\u68c0\u6d4b\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61671427","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/F4320325902","display_name":"Beijing Municipal Science and Technology Commission","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":74,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1553770567","https://openalex.org/W1801687142","https://openalex.org/W1875842236","https://openalex.org/W1932624639","https://openalex.org/W1976818984","https://openalex.org/W1982764079","https://openalex.org/W1986905809","https://openalex.org/W2010452185","https://openalex.org/W2027302138","https://openalex.org/W2035804851","https://openalex.org/W2041059661","https://openalex.org/W2079624250","https://openalex.org/W2081021369","https://openalex.org/W2102605133","https://openalex.org/W2107775979","https://openalex.org/W2114142878","https://openalex.org/W2115471590","https://openalex.org/W2125556102","https://openalex.org/W2125663122","https://openalex.org/W2131533724","https://openalex.org/W2133775549","https://openalex.org/W2143635852","https://openalex.org/W2149489931","https://openalex.org/W2152369758","https://openalex.org/W2155448681","https://openalex.org/W2161969291","https://openalex.org/W2163352848","https://openalex.org/W2168356304","https://openalex.org/W2169897982","https://openalex.org/W2194775991","https://openalex.org/W2200528286","https://openalex.org/W2340897893","https://openalex.org/W2342642695","https://openalex.org/W2465597433","https://openalex.org/W2477047852","https://openalex.org/W2490270993","https://openalex.org/W2497039038","https://openalex.org/W2508493797","https://openalex.org/W2531915888","https://openalex.org/W2551693289","https://openalex.org/W2565639579","https://openalex.org/W2594507094","https://openalex.org/W2610165754","https://openalex.org/W2613599172","https://openalex.org/W2613718673","https://openalex.org/W2792824754","https://openalex.org/W2809314052","https://openalex.org/W2883208628","https://openalex.org/W2883363148","https://openalex.org/W2884030607","https://openalex.org/W2895077992","https://openalex.org/W2895451584","https://openalex.org/W2953106684","https://openalex.org/W2962850098","https://openalex.org/W2963315052","https://openalex.org/W2963516811","https://openalex.org/W2963681621","https://openalex.org/W2963857746","https://openalex.org/W2963953305","https://openalex.org/W2963998989","https://openalex.org/W2964052344","https://openalex.org/W2964241181","https://openalex.org/W2964297960","https://openalex.org/W2969875432","https://openalex.org/W3106250896","https://openalex.org/W6620707391","https://openalex.org/W6638434749","https://openalex.org/W6646904105","https://openalex.org/W6653248712","https://openalex.org/W6722946945","https://openalex.org/W6745984080","https://openalex.org/W6753529042","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2914343065","https://openalex.org/W2802018156","https://openalex.org/W2963610131","https://openalex.org/W4313315626","https://openalex.org/W2101531944","https://openalex.org/W2913302899","https://openalex.org/W2922437833","https://openalex.org/W4312696271"],"abstract_inverted_index":{"Pedestrian":[0],"detection":[1,117,178],"in":[2,133,155,170],"the":[3,11,20,59,78,86,129,164,187],"wild":[4],"remains":[5],"a":[6,44,74,93],"challenging":[7],"problem":[8],"especially":[9],"when":[10],"scene":[12],"contains":[13],"significant":[14,195],"occlusion":[15,153],"and/or":[16],"low":[17,64],"resolution":[18,65],"of":[19,95,128,148,166,189],"pedestrians":[21,193],"to":[22,29,31,50,53,142],"be":[23,82],"detected.":[24],"Existing":[25],"methods":[26],"are":[27],"unable":[28],"adapt":[30],"these":[32],"difficult":[33],"cases":[34],"while":[35,197],"maintaining":[36,198],"acceptable":[37],"performance.":[38],"In":[39],"this":[40],"paper":[41],"we":[42,135],"propose":[43],"novel":[45],"feature":[46,55,96,106,112,130,161],"learning":[47],"model,":[48],"referred":[49],"as":[51,92],"CircleNet,":[52,134],"achieve":[54],"adaptation":[56,113,131],"by":[57],"mimicking":[58],"process":[60],"humans":[61],"looking":[62],"at":[63,73,110],"and":[66,98,114,121,151,181,191],"occluded":[67,190],"objects:":[68],"focusing":[69],"on":[70,144,175,201],"it":[71],"again,":[72],"finer":[75],"scale,":[76],"if":[77],"object":[79,116],"can":[80],"not":[81],"identified":[83],"clearly":[84],"for":[85,104],"first":[87],"time.":[88],"CircleNet":[89,159,185],"is":[90],"implemented":[91],"set":[94],"pyramids":[97],"uses":[99],"weight":[100],"sharing":[101],"path":[102],"augmentation":[103],"better":[105],"fusion.":[107],"It":[108],"targets":[109],"reciprocating":[111],"iterative":[115],"using":[118],"multiple":[119],"top-down":[120],"bottom-up":[122],"pathways.":[123],"To":[124],"take":[125],"full":[126],"advantage":[127],"capability":[132],"design":[136],"an":[137,171],"instance":[138],"decomposition":[139],"training":[140],"strategy":[141],"focus":[143],"detecting":[145],"pedestrian":[146,177],"instances":[147],"various":[149],"resolutions":[150],"different":[152],"levels":[154],"each":[156],"cycle.":[157],"Specifically,":[158],"implements":[160],"ensemble":[162],"with":[163,194],"idea":[165],"hard":[167],"negative":[168],"boosting":[169],"end-to-end":[172],"manner.":[173],"Experiments":[174],"two":[176],"datasets,":[179],"Caltech":[180],"CityPersons,":[182],"show":[183],"that":[184],"improves":[186],"performance":[188,200],"low-resolution":[192],"margins":[196],"good":[199],"normal":[202],"instances.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}