{"id":"https://openalex.org/W2888707409","doi":"https://doi.org/10.1109/snpd.2018.8441055","title":"Robust Pedestrian Detection via a Recursive Convolution Neural Network","display_name":"Robust Pedestrian Detection via a Recursive Convolution Neural Network","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2888707409","doi":"https://doi.org/10.1109/snpd.2018.8441055","mag":"2888707409"},"language":"en","primary_location":{"id":"doi:10.1109/snpd.2018.8441055","is_oa":false,"landing_page_url":"https://doi.org/10.1109/snpd.2018.8441055","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","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/A5048063093","display_name":"Tran Thi Dinh","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Tran Thi Dinh","raw_affiliation_strings":["School of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113128824","display_name":"Nguy\u1ec5n \u0110\u00ecnh Vinh","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Nguyen Dinh Vinh","raw_affiliation_strings":["School of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041337072","display_name":"Jeon Jae Wook","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jeon Jae Wook","raw_affiliation_strings":["School of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5048063093"],"corresponding_institution_ids":["https://openalex.org/I848706"],"apc_list":null,"apc_paid":null,"fwci":0.3134,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.61869452,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"3021","issue":null,"first_page":"281","last_page":"286"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10812","display_name":"Human Pose and Action Recognition","score":0.9980000257492065,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7985214591026306},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7859899997711182},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7570151090621948},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.614990770816803},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6066311001777649},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5987938642501831},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.5713698267936707},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4861897826194763},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4595150053501129},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.4288387596607208},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4269806444644928},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42204979062080383},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3663683235645294},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.3200417160987854}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7985214591026306},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7859899997711182},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7570151090621948},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.614990770816803},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6066311001777649},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5987938642501831},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.5713698267936707},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4861897826194763},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4595150053501129},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.4288387596607208},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4269806444644928},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42204979062080383},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3663683235645294},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.3200417160987854},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"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/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/snpd.2018.8441055","is_oa":false,"landing_page_url":"https://doi.org/10.1109/snpd.2018.8441055","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5299999713897705}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1423339008","https://openalex.org/W1482428446","https://openalex.org/W1524925681","https://openalex.org/W1536680647","https://openalex.org/W1650122911","https://openalex.org/W1902934009","https://openalex.org/W1971844566","https://openalex.org/W2026942141","https://openalex.org/W2084568123","https://openalex.org/W2096349671","https://openalex.org/W2097324787","https://openalex.org/W2104572919","https://openalex.org/W2109992539","https://openalex.org/W2146064909","https://openalex.org/W2156539399","https://openalex.org/W2158634074","https://openalex.org/W2160788207","https://openalex.org/W2161969291","https://openalex.org/W2167049458","https://openalex.org/W2300242332","https://openalex.org/W2490270993","https://openalex.org/W2497039038","https://openalex.org/W2546302380","https://openalex.org/W2551877753","https://openalex.org/W2613718673","https://openalex.org/W2963114950","https://openalex.org/W6620707391","https://openalex.org/W6639703010","https://openalex.org/W6674662834","https://openalex.org/W6682736280","https://openalex.org/W6683601019"],"related_works":["https://openalex.org/W2802018156","https://openalex.org/W2352028961","https://openalex.org/W2124097254","https://openalex.org/W4313315626","https://openalex.org/W2101531944","https://openalex.org/W3026806648","https://openalex.org/W2922437833","https://openalex.org/W2100052226","https://openalex.org/W4312696271","https://openalex.org/W2933098581"],"abstract_inverted_index":{"Pedestrian":[0],"detection":[1],"is":[2,176],"fundamental":[3],"challenge":[4],"for":[5,34,91,101],"computer":[6],"vision":[7],"which":[8,181],"requires":[9],"localizing":[10],"objects":[11,146],"within":[12],"an":[13,40],"image.":[14,51,135,150],"Convolutional":[15],"neural":[16,94,115,126,166],"networks":[17,35,116],"are":[18,111,158],"widely":[19],"used":[20],"in":[21,60,147],"object":[22,77,105],"recognition.":[23],"However,":[24],"ordinal":[25],"convolutional":[26,93,125,156],"methods":[27,73],"using":[28,55,137],"sliding":[29],"window":[30,50],"as":[31,88,161],"the":[32,61,70,76,83,89,99,133,139,148,155],"input":[33,90,134,149],"require":[36],"time":[37],"to":[38,54,64,74,117,163,169],"run":[39],"entire":[41],"image":[42],"and":[43,120,185],"can":[44,142,182],"only":[45],"handle":[46],"a":[47,56,92,164,177],"fixed":[48],"size":[49],"We":[52,96],"propose":[53],"region":[57,80],"proposal":[58],"based":[59],"V-disparity":[62,84],"method":[63],"obtain":[65,75],"prospect":[66],"regions,":[67],"instead":[68],"of":[69,132],"original":[71],"scanning":[72],"regions.":[78],"The":[79,124,151,174],"proposals":[81],"from":[82],"will":[85],"be":[86],"fed":[87],"network(CNN).":[95],"also":[97],"extend":[98],"CNN":[100,110],"more":[102],"effective":[103],"task":[104],"detection.":[106],"In":[107],"our":[108],"model,":[109],"combined":[112],"with":[113],"recursive":[114,165,180],"learn":[118],"features":[119,131,141,153],"classify":[121],"color":[122],"images.":[123],"network":[127,167],"layer":[128,157],"learns":[129],"low-level":[130],"By":[136],"CNN,":[138],"learned":[140,152],"represent":[143],"highly":[144],"variable":[145],"after":[154],"then":[159],"given":[160],"inputs":[162],"(RNN)":[168],"compose":[170],"higher":[171],"order":[172],"features.":[173],"RNN":[175],"multiple,":[178],"fixed-tree":[179],"combine":[183],"convolution":[184],"pooling":[186],"into":[187],"one":[188],"efficient":[189],"hierarchical":[190],"operation.":[191]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
