{"id":"https://openalex.org/W3162929401","doi":"https://doi.org/10.1109/icpr48806.2021.9413334","title":"Spatial-related and Scale-aware Network for Crowd Counting","display_name":"Spatial-related and Scale-aware Network for Crowd Counting","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3162929401","doi":"https://doi.org/10.1109/icpr48806.2021.9413334","mag":"3162929401"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9413334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","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/A5100440326","display_name":"Lei Li","orcid":"https://orcid.org/0000-0002-5374-7293"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lei Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056136429","display_name":"Yuan Dong","orcid":"https://orcid.org/0009-0004-8650-1603"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Dong","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101170186","display_name":"Hongliang Bai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongliang Bai","raw_affiliation_strings":["Beijing Faceall Co"],"affiliations":[{"raw_affiliation_string":"Beijing Faceall Co","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100440326"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04919935,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":null,"first_page":"1","last_page":"7"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.9890999794006348,"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":0.9879000186920166,"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":0.7595174312591553},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7259562611579895},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6696867942810059},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6468428373336792},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.6225036978721619},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6167126893997192},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5870541930198669},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4634556472301483},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4550027847290039},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4272345304489136},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.41358932852745056},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41346976161003113},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4003101885318756},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3911449611186981},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3553354740142822},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33522501587867737}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7595174312591553},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7259562611579895},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6696867942810059},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6468428373336792},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.6225036978721619},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6167126893997192},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5870541930198669},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4634556472301483},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4550027847290039},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4272345304489136},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.41358932852745056},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41346976161003113},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4003101885318756},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3911449611186981},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3553354740142822},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33522501587867737},{"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/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9413334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2072232009","https://openalex.org/W2088929512","https://openalex.org/W2096229530","https://openalex.org/W2120815373","https://openalex.org/W2145983039","https://openalex.org/W2147221461","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2165368973","https://openalex.org/W2207893099","https://openalex.org/W2412782625","https://openalex.org/W2463631526","https://openalex.org/W2592939477","https://openalex.org/W2741077351","https://openalex.org/W2808519136","https://openalex.org/W2884960332","https://openalex.org/W2886443245","https://openalex.org/W2895051362","https://openalex.org/W2914974653","https://openalex.org/W2963035940","https://openalex.org/W2963693541","https://openalex.org/W2963840672","https://openalex.org/W2963893037","https://openalex.org/W2964046724","https://openalex.org/W2964209782","https://openalex.org/W2964264515","https://openalex.org/W2991203386","https://openalex.org/W2996703886","https://openalex.org/W3004672782","https://openalex.org/W6668618559","https://openalex.org/W6681368121","https://openalex.org/W6684191040","https://openalex.org/W6748343077","https://openalex.org/W6753534557","https://openalex.org/W6754034799","https://openalex.org/W6754756387"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4313906399","https://openalex.org/W4321487865","https://openalex.org/W4321444604","https://openalex.org/W2811106690","https://openalex.org/W2936819511","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W2964954556","https://openalex.org/W3019910406"],"abstract_inverted_index":{"Crowd":[0],"counting":[1,119],"aims":[2],"to":[3,32,59,77],"estimate":[4],"the":[5,18,56,61,93,104,140],"number":[6],"of":[7,20,38,64,106],"people":[8,39],"in":[9],"images.":[10],"Although":[11],"promising":[12],"progresses":[13],"have":[14],"been":[15],"made":[16],"with":[17,92,112],"prevalence":[19],"deep":[21],"Convolutional":[22],"Neural":[23],"Networks,":[24],"there":[25],"still":[26],"remains":[27],"a":[28,48,67],"challenging":[29],"task":[30],"due":[31],"cluttered":[33],"backgrounds":[34],"and":[35,99],"varying":[36],"scales":[37],"within":[40],"an":[41],"image.":[42],"In":[43],"this":[44],"paper,":[45],"we":[46,109],"propose":[47],"learnable":[49],"spatial":[50,57],"attention":[51],"module":[52,72],"which":[53],"can":[54,90,134],"get":[55],"relations":[58],"diminish":[60],"negative":[62],"impact":[63],"backgrounds.":[65],"Besides,":[66],"dense":[68],"hybrid":[69],"dilated":[70],"convolution":[71],"is":[73],"also":[74],"brought":[75],"up":[76],"preserve":[78],"information":[79],"derived":[80],"from":[81],"varied":[82],"scales.":[83],"With":[84],"these":[85],"two":[86],"modules,":[87],"our":[88,107,131],"network":[89,133],"deal":[91],"problem":[94],"caused":[95],"by":[96],"scale":[97],"variance":[98],"background":[100],"interference.":[101],"To":[102],"demonstrate":[103],"effectiveness":[105],"method,":[108],"compare":[110],"it":[111],"state-of-the-art":[113],"algorithms":[114],"on":[115,138],"three":[116,141],"representative":[117],"crowd":[118],"benchmarks":[120],"(ShanghaiTech":[121],"[1],":[122],"UCF-QNRF":[123],"[2],":[124],"UCF_CC_50":[125],"[3]).":[126],"Experimental":[127],"results":[128],"show":[129],"that":[130],"proposed":[132],"achieve":[135],"significant":[136],"improvements":[137],"all":[139],"datasets.":[142]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
