{"id":"https://openalex.org/W2937366944","doi":"https://doi.org/10.1109/ccis.2018.8691384","title":"A pedestrian detection algorithm based on improved YOLOv2","display_name":"A pedestrian detection algorithm based on improved YOLOv2","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2937366944","doi":"https://doi.org/10.1109/ccis.2018.8691384","mag":"2937366944"},"language":"en","primary_location":{"id":"doi:10.1109/ccis.2018.8691384","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccis.2018.8691384","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)","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/A5100406050","display_name":"Ziwei Liu","orcid":"https://orcid.org/0000-0002-4220-5958"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ziwei Liu","raw_affiliation_strings":["Wuhan University of Technology, Wuhan 430070, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Technology, Wuhan 430070, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102001806","display_name":"Ying Shi","orcid":"https://orcid.org/0000-0002-6495-305X"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Shi","raw_affiliation_strings":["Wuhan University of Technology, Wuhan 430070, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Technology, Wuhan 430070, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101020059","display_name":"Mingjun Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingjun Sun","raw_affiliation_strings":["Wuhan University of Technology, Wuhan 430070, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Technology, Wuhan 430070, China","institution_ids":["https://openalex.org/I196699116"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100406050"],"corresponding_institution_ids":["https://openalex.org/I196699116"],"apc_list":null,"apc_paid":null,"fwci":0.1045,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.49568076,"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":"488","last_page":"492"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9988999962806702,"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":0.9988999962806702,"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":0.9977999925613403,"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/T13918","display_name":"Advanced Data and IoT Technologies","score":0.9832000136375427,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/computer-science","display_name":"Computer science","score":0.7157626748085022},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.6686700582504272},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6126143336296082},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5293674468994141},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.5201892852783203},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.49971723556518555},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.43078821897506714},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40395623445510864},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3814198076725006},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3070359230041504},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12554556131362915}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7157626748085022},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.6686700582504272},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6126143336296082},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5293674468994141},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.5201892852783203},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.49971723556518555},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.43078821897506714},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40395623445510864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3814198076725006},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3070359230041504},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12554556131362915},{"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},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccis.2018.8691384","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccis.2018.8691384","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1608462934","https://openalex.org/W2036989445","https://openalex.org/W2097324787","https://openalex.org/W2102605133","https://openalex.org/W2152473410","https://openalex.org/W2161969291","https://openalex.org/W2169671170","https://openalex.org/W2570343428","https://openalex.org/W2963037989","https://openalex.org/W3106250896","https://openalex.org/W6659571732","https://openalex.org/W6674662834","https://openalex.org/W6675026286"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2802018156","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W2922437833","https://openalex.org/W4223892596","https://openalex.org/W4312696271","https://openalex.org/W2933098581","https://openalex.org/W2556125083"],"abstract_inverted_index":{"For":[0],"improving":[1],"the":[2,43,47,60,68,78,81,89,97,105,116],"accuracy":[3,107],"of":[4,21,26,46,83,91,94],"pedestrian":[5,48],"detection,":[6],"an":[7],"improved":[8,84],"algorithm":[9,38,85],"based":[10],"on":[11,77],"YOLOv2":[12,32],"network":[13],"framework":[14],"is":[15,39],"proposed.":[16],"Usually,":[17],"a":[18],"large":[19],"number":[20],"redundant":[22,69],"candidate":[23],"proposal":[24,50,61,71],"boxes":[25],"detected":[27,121],"pedestrians":[28],"exist":[29],"via":[30],"using":[31],"framework.":[33],"In":[34],"this":[35],"paper,":[36],"clustering":[37],"adapted":[40],"to":[41,58,66,124],"obtain":[42],"priori":[44],"knowledge":[45],"target":[49],"box":[51,62],"scale":[52],"and":[53,65,99,104,113,119],"aspect":[54],"ratio":[55],"in":[56],"order":[57],"formulate":[59],"filtering":[63],"rules":[64],"remove":[67],"filtered":[70],"boxes.":[72],"Experimental":[73],"results":[74],"show":[75],"that":[76],"KITTI":[79],"dataset,":[80],"performance":[82],"will":[86],"degrade":[87],"with":[88],"increase":[90],"difficulty":[92],"level":[93],"targets,":[95],"but":[96],"precision":[98],"recall":[100],"rate":[101],"increases":[102,108],"obviously":[103],"detection":[106],"respectively":[109],"by":[110],"9.03%,":[111],"6.37%":[112],"5.91%":[114],"for":[115],"easily,":[117],"moderately":[118],"hardly":[120],"targets":[122],"compared":[123],"YOLOv2.":[125]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
