{"id":"https://openalex.org/W2962852859","doi":"https://doi.org/10.1109/access.2019.2929675","title":"SMCA-CNN: Learning a Semantic Mask and Cross-Scale Adaptive Feature for Robust Crowd Counting","display_name":"SMCA-CNN: Learning a Semantic Mask and Cross-Scale Adaptive Feature for Robust Crowd Counting","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2962852859","doi":"https://doi.org/10.1109/access.2019.2929675","mag":"2962852859"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2929675","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2929675","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08765698.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08765698.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067368590","display_name":"Guoshuai Wang","orcid":"https://orcid.org/0000-0003-1789-1383"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guoshuai Wang","raw_affiliation_strings":["ADSPLAB, School of ECE, Peking University, Shenzhen, China","Peng Cheng Laboratory, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"ADSPLAB, School of ECE, Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006791117","display_name":"Yue Zou","orcid":"https://orcid.org/0000-0003-0968-1386"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Zou","raw_affiliation_strings":["ADSPLAB, School of ECE, Peking University, Shenzhen, China","Peng Cheng Laboratory, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"ADSPLAB, School of ECE, Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042718225","display_name":"Zirui Li","orcid":"https://orcid.org/0000-0001-7056-4264"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zirui Li","raw_affiliation_strings":["ADSPLAB, School of ECE, Peking University, Shenzhen, China","Peng Cheng Laboratory, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"ADSPLAB, School of ECE, Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102809481","display_name":"Dongming Yang","orcid":"https://orcid.org/0000-0002-1478-9642"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongming Yang","raw_affiliation_strings":["ADSPLAB, School of ECE, Peking University, Shenzhen, China","Peng Cheng Laboratory, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"ADSPLAB, School of ECE, Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067368590"],"corresponding_institution_ids":["https://openalex.org/I20231570","https://openalex.org/I4210136793"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.1012,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.42493411,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"7","issue":null,"first_page":"168495","last_page":"168506"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.9955999851226807,"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/computer-science","display_name":"Computer science","score":0.7830672264099121},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7511371970176697},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6765142679214478},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6482210755348206},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5993085503578186},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5461422801017761},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5358537435531616},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4571509063243866}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7830672264099121},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7511371970176697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6765142679214478},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6482210755348206},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5993085503578186},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5461422801017761},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5358537435531616},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4571509063243866},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2929675","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2929675","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08765698.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7a16afd39b494eb4b4b33b10128a4ead","is_oa":true,"landing_page_url":"https://doaj.org/article/7a16afd39b494eb4b4b33b10128a4ead","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 168495-168506 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2929675","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2929675","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08765698.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.5199999809265137}],"awards":[],"funders":[{"id":"https://openalex.org/F4320327511","display_name":"Development and Reform Commission of Shenzhen Municipality","ror":"https://ror.org/03jmg4515"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2962852859.pdf","grobid_xml":"https://content.openalex.org/works/W2962852859.grobid-xml"},"referenced_works_count":73,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1485009520","https://openalex.org/W1686810756","https://openalex.org/W1903029394","https://openalex.org/W1910776219","https://openalex.org/W1976959044","https://openalex.org/W2006170882","https://openalex.org/W2013039598","https://openalex.org/W2052355211","https://openalex.org/W2072232009","https://openalex.org/W2097324787","https://openalex.org/W2108598243","https://openalex.org/W2120815373","https://openalex.org/W2122243179","https://openalex.org/W2123175289","https://openalex.org/W2130751540","https://openalex.org/W2133665775","https://openalex.org/W2145983039","https://openalex.org/W2147221461","https://openalex.org/W2155916750","https://openalex.org/W2156303437","https://openalex.org/W2161841955","https://openalex.org/W2161969291","https://openalex.org/W2194775991","https://openalex.org/W2197234429","https://openalex.org/W2207893099","https://openalex.org/W2331128040","https://openalex.org/W2394843433","https://openalex.org/W2463631526","https://openalex.org/W2516908515","https://openalex.org/W2517615595","https://openalex.org/W2519281173","https://openalex.org/W2528639018","https://openalex.org/W2541389513","https://openalex.org/W2565639579","https://openalex.org/W2741077351","https://openalex.org/W2792947308","https://openalex.org/W2798489385","https://openalex.org/W2798490576","https://openalex.org/W2798781811","https://openalex.org/W2810417872","https://openalex.org/W2883929025","https://openalex.org/W2884960332","https://openalex.org/W2886443245","https://openalex.org/W2895051362","https://openalex.org/W2948513880","https://openalex.org/W2950981687","https://openalex.org/W2953106684","https://openalex.org/W2962720716","https://openalex.org/W2963035940","https://openalex.org/W2963446712","https://openalex.org/W2963840672","https://openalex.org/W2963881378","https://openalex.org/W2964046724","https://openalex.org/W2964209782","https://openalex.org/W2967776630","https://openalex.org/W2990524248","https://openalex.org/W3097096317","https://openalex.org/W3106250896","https://openalex.org/W6620707391","https://openalex.org/W6637373629","https://openalex.org/W6674662834","https://openalex.org/W6678246861","https://openalex.org/W6681368121","https://openalex.org/W6682864246","https://openalex.org/W6696085341","https://openalex.org/W6700594562","https://openalex.org/W6702130928","https://openalex.org/W6728547873","https://openalex.org/W6752492241","https://openalex.org/W6757472186","https://openalex.org/W6763421409","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Density-based":[0],"crowd":[1,65,109,146,188],"counting":[2,52,89,189],"methods":[3,29],"with":[4,198,224],"deep":[5,159],"convolutional":[6],"neural":[7],"network":[8],"(CNN)":[9],"have":[10],"achieved":[11],"the":[12,15,18,25,46,51,57,73,83,88,93,105,112,118,125,130,141,145,150,154,164,177,192,205,218,225,243],"state":[13],"of":[14,27,59,72,108,157,204,220],"art":[16],"on":[17,213,235,242],"challenging":[19,215],"datasets.":[20],"Experimental":[21],"results":[22],"showed":[23],"that":[24],"performance":[26],"these":[28],"suffers":[30],"from":[31,117,153],"two":[32,84],"problems:":[33],"1)":[34],"Background":[35],"interference":[36],"problem:":[37,56],"there":[38],"are":[39,122],"some":[40],"estimated":[41],"spurious":[42],"density":[43,74,120],"values":[44],"in":[45,64,144],"background":[47],"regions":[48],"which":[49,67],"degrade":[50],"accuracy.":[53,90],"2)":[54],"Cross-scale":[55,169],"scale":[58],"human":[60],"heads":[61],"varies":[62],"greatly":[63],"images":[66,110,147],"lead":[68],"to":[69,81,103,138],"poorer":[70],"quality":[71],"maps.":[75],"In":[76,176],"this":[77],"study,":[78],"we":[79,132],"aim":[80],"address":[82,92],"problems":[85],"for":[86],"enhancing":[87],"To":[91,128,161],"former,":[94],"a":[95,134,158,168,199],"light":[96],"semantic":[97,106,114],"mask":[98],"module":[99],"(SMM)":[100],"is":[101,173,194],"proposed":[102],"learn":[104],"masks":[107,115],"where":[111],"ground-truth":[113,119],"generated":[116],"map":[121],"taken":[123],"as":[124],"supervision":[126],"information.":[127],"tackle":[129],"latter,":[131],"propose":[133],"span":[135],"architecture":[136],"(SA)":[137],"effectively":[139],"capture":[140],"large-scale-variation":[142],"information":[143],"by":[148],"building":[149],"cross-scale":[151,166],"features":[152],"pyramidal":[155],"structure":[156],"CNN.":[160],"adaptively":[162],"leverage":[163],"salient":[165],"features,":[167],"Adaptive":[170],"Module":[171],"(CAM)":[172],"delicately":[174],"designed.":[175],"end,":[178],"integrating":[179],"all":[180],"elements":[181],"above,":[182],"an":[183],"end-to-end":[184],"trainable":[185],"and":[186,196,208,238],"single-column":[187],"model":[190,230],"called":[191],"SMCA-CNN":[193],"developed":[195],"trained":[197],"joint":[200],"loss":[201,207],"function":[202],"consisting":[203],"cross-entropy":[206],"Euclidean":[209],"loss.":[210],"Extensive":[211],"experiments":[212],"five":[214],"datasets":[216],"demonstrate":[217],"effectiveness":[219],"our":[221,229],"SMCA-CNN.":[222],"Compared":[223],"previous":[226],"state-of-the-art":[227],"methods,":[228],"achieves":[231],"17.1%":[232],"lower":[233,240],"MAE":[234,241],"dataset":[236,246],"UCF_CC_50":[237],"23.6%":[239],"newly":[244],"published":[245],"UCF-QNRF.":[247]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
