{"id":"https://openalex.org/W2921213323","doi":"https://doi.org/10.1109/istel.2018.8661043","title":"Deep Learning based on CNN for Pedestrian Detection: An Overview and Analysis","display_name":"Deep Learning based on CNN for Pedestrian Detection: An Overview and Analysis","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2921213323","doi":"https://doi.org/10.1109/istel.2018.8661043","mag":"2921213323"},"language":"en","primary_location":{"id":"doi:10.1109/istel.2018.8661043","is_oa":false,"landing_page_url":"https://doi.org/10.1109/istel.2018.8661043","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 9th International Symposium on Telecommunications (IST)","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/A5019816926","display_name":"Mahmoud Saeidi","orcid":"https://orcid.org/0000-0001-5495-9442"},"institutions":[{"id":"https://openalex.org/I80543232","display_name":"K.N.Toosi University of Technology","ror":"https://ror.org/0433abe34","country_code":"IR","type":"education","lineage":["https://openalex.org/I80543232"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Mahmoud Saeidi","raw_affiliation_strings":["Faculty of Computer Engeenering, K. N. Toosi University of Technology, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Faculty of Computer Engeenering, K. N. Toosi University of Technology, Tehran, Iran","institution_ids":["https://openalex.org/I80543232"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031000559","display_name":"Ali Ahmadi","orcid":"https://orcid.org/0000-0003-4211-6258"},"institutions":[{"id":"https://openalex.org/I80543232","display_name":"K.N.Toosi University of Technology","ror":"https://ror.org/0433abe34","country_code":"IR","type":"education","lineage":["https://openalex.org/I80543232"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Ali Ahmadi","raw_affiliation_strings":["School of Computer Science, Faculty of Computer Engeenering Institute for Research in Fundamental Science (IPM), K. N. Toosi University of Technology, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Faculty of Computer Engeenering Institute for Research in Fundamental Science (IPM), K. N. Toosi University of Technology, Tehran, Iran","institution_ids":["https://openalex.org/I80543232"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5019816926"],"corresponding_institution_ids":["https://openalex.org/I80543232"],"apc_list":null,"apc_paid":null,"fwci":0.2089,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.58225883,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"108","last_page":"112"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9980000257492065,"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"}},{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9927999973297119,"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":0.9184662103652954},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.8198283910751343},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.7729365825653076},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7559937834739685},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7474201321601868},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7248831987380981},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.6990790963172913},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5080280303955078},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5027172565460205},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4394186735153198},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3096347451210022},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13257944583892822}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.9184662103652954},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8198283910751343},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7729365825653076},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7559937834739685},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7474201321601868},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7248831987380981},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.6990790963172913},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5080280303955078},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5027172565460205},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4394186735153198},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3096347451210022},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13257944583892822},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/istel.2018.8661043","is_oa":false,"landing_page_url":"https://doi.org/10.1109/istel.2018.8661043","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 9th International Symposium on Telecommunications (IST)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5099999904632568,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W7746136","https://openalex.org/W639708223","https://openalex.org/W1475617732","https://openalex.org/W1536680647","https://openalex.org/W1555385401","https://openalex.org/W1650122911","https://openalex.org/W1686810756","https://openalex.org/W1903127635","https://openalex.org/W1976818984","https://openalex.org/W2010181071","https://openalex.org/W2010340098","https://openalex.org/W2034779469","https://openalex.org/W2066624635","https://openalex.org/W2081021369","https://openalex.org/W2084997728","https://openalex.org/W2088049833","https://openalex.org/W2097117768","https://openalex.org/W2102605133","https://openalex.org/W2107775979","https://openalex.org/W2125556102","https://openalex.org/W2130306094","https://openalex.org/W2150066425","https://openalex.org/W2150298366","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2170101770","https://openalex.org/W2179352600","https://openalex.org/W2194775991","https://openalex.org/W2200528286","https://openalex.org/W2206858481","https://openalex.org/W2210976041","https://openalex.org/W2405365196","https://openalex.org/W2613718673","https://openalex.org/W2962835968","https://openalex.org/W2963542991","https://openalex.org/W6600313631","https://openalex.org/W6620707391","https://openalex.org/W6629368666","https://openalex.org/W6633201533","https://openalex.org/W6636787326","https://openalex.org/W6637373629","https://openalex.org/W6676338569","https://openalex.org/W6679349572","https://openalex.org/W6684191040","https://openalex.org/W6688059459"],"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/W2969228573","https://openalex.org/W2963690996","https://openalex.org/W2922437833","https://openalex.org/W4312696271","https://openalex.org/W4223892596"],"abstract_inverted_index":{"Pedestrian":[0],"detection":[1,61,76,116,137],"methods":[2,32],"based":[3,35,63,93,106,138],"on":[4,36,46,64,94,107,139],"deep":[5,30,114,123,135],"learning":[6,31],"can":[7],"automatically":[8],"learn":[9,20],"features":[10],"in":[11,59,74],"unsupervised":[12],"or":[13],"supervised":[14],"fashions":[15],"and":[16,70,96,100,122,128],"are":[17],"capable":[18],"to":[19,26,55],"qualified":[21],"high":[22],"level":[23],"feature":[24],"representations":[25],"detect":[27],"pedestrian.":[28],"Recently,":[29],"chiefly":[33],"algorithms":[34,62,105,117],"DCNN":[37,73,95],"(Deep":[38],"Convolutional":[39],"Neural":[40],"Network)":[41],"have":[42],"made":[43],"outstanding":[44],"achievements":[45],"pedestrian":[47,60,75,115,136],"detection.":[48],"The":[49],"aim":[50],"of":[51,72,89,126],"this":[52,83],"paper":[53,84,112],"is":[54],"review":[56],"the":[57,78,111,140],"state-of-the-art":[58],"DCNN.":[65],"Also,":[66],"we":[67],"emphasize":[68],"contributions":[69],"challenges":[71],"from":[77,118],"recent":[79,98,104],"researches.":[80],"In":[81],"fact,":[82],"first":[85],"overviews":[86],"a":[87,130],"number":[88],"well-known":[90],"training":[91,119,132],"approaches":[92],"their":[97],"development,":[99],"then":[101],"briefly":[102],"describes":[103],"these":[108],"approaches.":[109],"Ultimately,":[110],"analyzes":[113],"approach,":[120],"categorization,":[121],"model":[124],"points":[125],"view,":[127],"proposes":[129],"novel":[131],"method":[133],"for":[134],"analysis.":[141]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
