{"id":"https://openalex.org/W2945786113","doi":"https://doi.org/10.1109/tifs.2019.2916592","title":"JCS-Net: Joint Classification and Super-Resolution Network for Small-Scale Pedestrian Detection in Surveillance Images","display_name":"JCS-Net: Joint Classification and Super-Resolution Network for Small-Scale Pedestrian Detection in Surveillance Images","publication_year":2019,"publication_date":"2019-05-14","ids":{"openalex":"https://openalex.org/W2945786113","doi":"https://doi.org/10.1109/tifs.2019.2916592","mag":"2945786113"},"language":"en","primary_location":{"id":"doi:10.1109/tifs.2019.2916592","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2019.2916592","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"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 Information Forensics and Security","raw_type":"journal-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/A5086887025","display_name":"Yanwei Pang","orcid":"https://orcid.org/0000-0001-6670-3727"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanwei Pang","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0001-6670-3727","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037129512","display_name":"Jiale Cao","orcid":"https://orcid.org/0000-0002-5160-6841"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiale Cao","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-5160-6841","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100370416","display_name":"Jian Wang","orcid":"https://orcid.org/0000-0002-2388-6831"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Wang","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046605531","display_name":"Jungong Han","orcid":"https://orcid.org/0000-0003-4361-956X"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jungong Han","raw_affiliation_strings":["WMG Data Science Group, University of Warwick, Coventry, U.K"],"raw_orcid":"https://orcid.org/0000-0003-4361-956X","affiliations":[{"raw_affiliation_string":"WMG Data Science Group, University of Warwick, Coventry, U.K","institution_ids":["https://openalex.org/I39555362"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.0619,"has_fulltext":false,"cited_by_count":98,"citation_normalized_percentile":{"value":0.96347173,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"14","issue":"12","first_page":"3322","last_page":"3331"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9987000226974487,"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/T11105","display_name":"Advanced Image Processing Techniques","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"}}],"keywords":[{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.8910859823226929},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8242567181587219},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6722455024719238},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6603877544403076},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6390156745910645},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6108371019363403},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5565716028213501},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5206465125083923},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4940071702003479},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4738762080669403},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.43803900480270386},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38568371534347534},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36093902587890625},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34318220615386963},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.060715317726135254},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.058408498764038086},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.05586501955986023}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8910859823226929},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8242567181587219},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6722455024719238},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6603877544403076},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6390156745910645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6108371019363403},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5565716028213501},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5206465125083923},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4940071702003479},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4738762080669403},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.43803900480270386},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38568371534347534},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36093902587890625},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34318220615386963},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.060715317726135254},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.058408498764038086},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.05586501955986023},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tifs.2019.2916592","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2019.2916592","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"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 Information Forensics and Security","raw_type":"journal-article"},{"id":"pmh:oai:wrap.warwick.ac.uk:119656","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400665","display_name":"Warwick Research Archive Portal (University of Warwick)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39555362","host_organization_name":"University of Warwick","host_organization_lineage":["https://openalex.org/I39555362"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6000000238418579,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G11847706","display_name":null,"funder_award_id":"61632018","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G139894655","display_name":null,"funder_award_id":"2018M641647","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G3586269372","display_name":"\u57fa\u4e8e\u7a00\u758f\u8868\u793a\u548c\u89c6\u89c9\u6ce8\u610f\u673a\u5236\u7684\u591a\u4f20\u611f\u5668\u56fe\u50cf/\u89c6\u9891\u878d\u5408","funder_award_id":"61773301","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7598959869","display_name":null,"funder_award_id":"BX20180214","funder_id":"https://openalex.org/F4320335768","funder_display_name":"National Postdoctoral Program for Innovative Talents"}],"funders":[{"id":"https://openalex.org/F4320307798","display_name":"Nokia","ror":"https://ror.org/04pkc8m17"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320335768","display_name":"National Postdoctoral Program for Innovative Talents","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":81,"referenced_works":["https://openalex.org/W345900524","https://openalex.org/W639708223","https://openalex.org/W1475617732","https://openalex.org/W1536680647","https://openalex.org/W1650122911","https://openalex.org/W1686810756","https://openalex.org/W1885185971","https://openalex.org/W1903127635","https://openalex.org/W1963789929","https://openalex.org/W1976818984","https://openalex.org/W2031454541","https://openalex.org/W2034779469","https://openalex.org/W2046561086","https://openalex.org/W2060503210","https://openalex.org/W2063626761","https://openalex.org/W2074777933","https://openalex.org/W2081021369","https://openalex.org/W2088049833","https://openalex.org/W2102605133","https://openalex.org/W2107775979","https://openalex.org/W2108598243","https://openalex.org/W2125556102","https://openalex.org/W2150066425","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2162741153","https://openalex.org/W2163605009","https://openalex.org/W2168356304","https://openalex.org/W2170101770","https://openalex.org/W2170110077","https://openalex.org/W2200528286","https://openalex.org/W2231359080","https://openalex.org/W2242218935","https://openalex.org/W2274131182","https://openalex.org/W2276051145","https://openalex.org/W2288122362","https://openalex.org/W2291533986","https://openalex.org/W2302502886","https://openalex.org/W2307425316","https://openalex.org/W2312004824","https://openalex.org/W2342242867","https://openalex.org/W2468368736","https://openalex.org/W2490270993","https://openalex.org/W2497039038","https://openalex.org/W2518599539","https://openalex.org/W2520164769","https://openalex.org/W2529780763","https://openalex.org/W2565639579","https://openalex.org/W2580773671","https://openalex.org/W2594605677","https://openalex.org/W2607041014","https://openalex.org/W2609144136","https://openalex.org/W2613599172","https://openalex.org/W2613718673","https://openalex.org/W2625219738","https://openalex.org/W2747898905","https://openalex.org/W2792824754","https://openalex.org/W2794849006","https://openalex.org/W2795024892","https://openalex.org/W2799120945","https://openalex.org/W2889709668","https://openalex.org/W2896155169","https://openalex.org/W2963315052","https://openalex.org/W2964046669","https://openalex.org/W2964101377","https://openalex.org/W3097096317","https://openalex.org/W3098140381","https://openalex.org/W3105196683","https://openalex.org/W3105814091","https://openalex.org/W4293418191","https://openalex.org/W6620707391","https://openalex.org/W6636787326","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6684811926","https://openalex.org/W6723816956","https://openalex.org/W6726381175","https://openalex.org/W6734514467","https://openalex.org/W6749094794","https://openalex.org/W6749232623","https://openalex.org/W6750161017"],"related_works":["https://openalex.org/W4288602136","https://openalex.org/W2992551472","https://openalex.org/W2802679057","https://openalex.org/W2996655622","https://openalex.org/W2128694549","https://openalex.org/W4384701254","https://openalex.org/W2067373798","https://openalex.org/W2014339256","https://openalex.org/W3142777113","https://openalex.org/W2497633036"],"abstract_inverted_index":{"While":[0],"convolutional":[1],"neural":[2],"network":[3,82],"(CNN)-based":[4],"pedestrian":[5,89,149,173],"detection":[6,174],"methods":[7],"have":[8],"proven":[9],"to":[10,38,48,61,139],"be":[11],"successful":[12],"in":[13,100],"various":[14],"applications,":[15],"detecting":[16,76],"small-scale":[17,31,59,69,77,88],"pedestrians":[18,32,55,60],"from":[19],"surveillance":[20],"images":[21],"is":[22,28,85,175],"still":[23],"challenging.":[24],"The":[25,143],"major":[26],"reason":[27],"that":[29],"the":[30,39,50,53,57,64,68,73,93,97,107,115,125,141,147,152,156,159,165,180],"lack":[33],"much":[34],"detailed":[35,65,122],"information":[36,66,123],"compared":[37],"large-scale":[40,54],"pedestrians.":[41,78],"To":[42,162],"solve":[43],"this":[44],"problem,":[45],"we":[46],"propose":[47],"utilize":[49],"relationship":[51],"between":[52],"and":[56,96,109,114,131,151],"corresponding":[58],"help":[62],"recover":[63,119],"of":[67,75,158],"pedestrians,":[70],"thus":[71],"improving":[72],"performance":[74],"Specifically,":[79],"a":[80,101,105],"unified":[81,102],"(called":[83],"JCS-Net)":[84],"proposed":[86,160],"for":[87,124,172],"detection,":[90,166],"which":[91,178],"integrates":[92],"classification":[94,110],"task":[95,99],"super-resolution":[98,108,116],"framework.":[103],"As":[104],"result,":[106],"are":[111,137],"fully":[112],"engaged,":[113],"sub-network":[117],"can":[118],"some":[120],"useful":[121],"subsequent":[126],"classification.":[127],"Based":[128],"on":[129,146,170],"HOG+LUV":[130],"JCS-Net,":[132],"multi-layer":[133],"channel":[134],"features":[135],"(MCF)":[136],"constructed":[138],"train":[140],"detector.":[142],"experimental":[144],"results":[145],"Caltech":[148],"dataset":[150],"KITTI":[153],"benchmark":[154],"demonstrate":[155],"effectiveness":[157],"method.":[161],"further":[163],"enhance":[164],"multi-scale":[167],"MCF":[168],"based":[169],"JCS-Net":[171],"also":[176],"proposed,":[177],"achieves":[179],"state-of-the-art":[181],"performance.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":16},{"year":2019,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}