{"id":"https://openalex.org/W2533926658","doi":"https://doi.org/10.1109/acpr.2015.7486548","title":"1000fps human segmentation with deep convolutional neural networks","display_name":"1000fps human segmentation with deep convolutional neural networks","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2533926658","doi":"https://doi.org/10.1109/acpr.2015.7486548","mag":"2533926658"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2015.7486548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","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/A5068998602","display_name":"Chunfeng Song","orcid":"https://orcid.org/0000-0003-1223-3242"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chunfeng Song","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, Beijing, China","North China Electric Power University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210094879","https://openalex.org/I19820366"]},{"raw_affiliation_string":"North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024579621","display_name":"Yongzhen Huang","orcid":"https://orcid.org/0000-0003-4389-9805"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongzhen Huang","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210094879","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100657484","display_name":"Zhenyu Wang","orcid":"https://orcid.org/0000-0002-4259-3073"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyu Wang","raw_affiliation_strings":["North China Electric Power University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057742263","display_name":"Liang Wang","orcid":"https://orcid.org/0009-0004-4708-1079"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Wang","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210094879","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5068998602"],"corresponding_institution_ids":["https://openalex.org/I153473198","https://openalex.org/I19820366","https://openalex.org/I4210094879"],"apc_list":null,"apc_paid":null,"fwci":1.6569,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.89752437,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"474","last_page":"478"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9948999881744385,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9939000010490417,"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/segmentation","display_name":"Segmentation","score":0.8181661367416382},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8057777285575867},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7378807663917542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7132108211517334},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5870169997215271},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5798186659812927},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5300968289375305},{"id":"https://openalex.org/keywords/champion","display_name":"Champion","score":0.5229050517082214},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5133799314498901},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4610549211502075},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.4588528275489807},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4487883746623993},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.42574721574783325},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4159315228462219},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.378610759973526},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3243085741996765}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8181661367416382},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8057777285575867},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7378807663917542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7132108211517334},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5870169997215271},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5798186659812927},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5300968289375305},{"id":"https://openalex.org/C2780465443","wikidata":"https://www.wikidata.org/wiki/Q852061","display_name":"Champion","level":2,"score":0.5229050517082214},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5133799314498901},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4610549211502075},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.4588528275489807},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4487883746623993},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.42574721574783325},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4159315228462219},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.378610759973526},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3243085741996765},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acpr.2015.7486548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320316083","display_name":"Tencent","ror":"https://ror.org/00hhjss72"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2004935438","https://openalex.org/W2097117768","https://openalex.org/W2100495367","https://openalex.org/W2102605133","https://openalex.org/W2121295188","https://openalex.org/W2125849446","https://openalex.org/W2145094598","https://openalex.org/W2151862441","https://openalex.org/W2153410696","https://openalex.org/W2163605009","https://openalex.org/W2535516436","https://openalex.org/W2962835968","https://openalex.org/W2997574889","https://openalex.org/W6637373629","https://openalex.org/W6674914833","https://openalex.org/W6678684981","https://openalex.org/W6681096077","https://openalex.org/W6681843719","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2945274617","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W2055202857","https://openalex.org/W2371519352","https://openalex.org/W4205800335","https://openalex.org/W2386644571","https://openalex.org/W2372421320","https://openalex.org/W2551987074"],"abstract_inverted_index":{"Efficiency":[0],"and":[1,30,46,77,106],"effectiveness":[2],"are":[3],"two":[4],"key":[5],"factors":[6],"to":[7],"evaluate":[8],"a":[9,44,61],"human":[10,32,49],"segmentation":[11,33,50,92,102],"algorithm":[12],"for":[13,138],"real":[14],"vision":[15],"applications.":[16,140],"However,":[17],"most":[18],"existing":[19],"algorithms":[20,103],"only":[21],"focus":[22],"on":[23,64,86],"one":[24],"of":[25,74,89],"them.":[26],"That":[27],"is,":[28],"fast":[29],"accurate":[31,48],"is":[34,110,120],"not":[35],"yet":[36],"well":[37],"addressed.":[38],"In":[39],"this":[40],"paper,":[41],"we":[42],"propose":[43],"super-fast":[45],"highly":[47],"method":[51,124],"with":[52,80],"very":[53,115],"deep":[54],"convolutional":[55],"neural":[56],"networks.":[57],"We":[58],"also":[59],"provide":[60],"comprehensive":[62],"study":[63],"the":[65,87,97,114],"proposed":[66,98],"approach,":[67],"including":[68],"different":[69,81],"net":[70],"structures,":[71],"various":[72],"techniques":[73],"alleviating":[75],"over-fitting,":[76],"performance":[78],"enhancement":[79],"extra":[82],"data.":[83],"Experimental":[84],"results":[85],"database":[88],"Baidu":[90],"people":[91],"competition":[93],"[1]":[94],"demonstrate":[95],"that":[96,122,133],"model":[99],"outperforms":[100],"traditional":[101],"in":[104],"accuracy":[105],"speed.":[107],"Although":[108],"it":[109,119,134],"slightly":[111],"worse":[112],"than":[113,128],"complex":[116],"champion":[117],"algorithm,":[118],"encouraging":[121],"our":[123],"can":[125],"obtain":[126],"more":[127],"10,000":[129],"times":[130],"acceleration,":[131],"showing":[132],"has":[135],"great":[136],"potential":[137],"practical":[139]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}