{"id":"https://openalex.org/W4395057350","doi":"https://doi.org/10.1145/3638884.3638886","title":"Multi-scale Pedestrian Detection Based on Attention Mechanism and Feature Fusion","display_name":"Multi-scale Pedestrian Detection Based on Attention Mechanism and Feature Fusion","publication_year":2023,"publication_date":"2023-12-14","ids":{"openalex":"https://openalex.org/W4395057350","doi":"https://doi.org/10.1145/3638884.3638886"},"language":"en","primary_location":{"id":"doi:10.1145/3638884.3638886","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3638884.3638886","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 9th International Conference on Communication and Information Processing","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/A5102945386","display_name":"Yuanyi Huang","orcid":"https://orcid.org/0009-0007-2919-8646"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Yuanyi Huang","raw_affiliation_strings":["Department of Computing, Faculty of Engineering, The Hong Kong Polytechnic University, China"],"raw_orcid":"https://orcid.org/0009-0007-2919-8646","affiliations":[{"raw_affiliation_string":"Department of Computing, Faculty of Engineering, The Hong Kong Polytechnic University, China","institution_ids":["https://openalex.org/I14243506"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5102945386"],"corresponding_institution_ids":["https://openalex.org/I14243506"],"apc_list":null,"apc_paid":null,"fwci":0.1177,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.46875571,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"6","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9983999729156494,"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":0.9976999759674072,"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/pedestrian-detection","display_name":"Pedestrian detection","score":0.9272415041923523},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8007380962371826},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.7884312868118286},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6776704788208008},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6325827836990356},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6312505006790161},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5549500584602356},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5416621565818787},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5258710384368896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4639292359352112},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.43636518716812134},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4150995910167694},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3885430097579956},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12804266810417175}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.9272415041923523},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8007380962371826},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7884312868118286},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6776704788208008},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6325827836990356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6312505006790161},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5549500584602356},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5416621565818787},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5258710384368896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4639292359352112},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.43636518716812134},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4150995910167694},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3885430097579956},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12804266810417175},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3638884.3638886","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3638884.3638886","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 9th International Conference on Communication and Information Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7900000214576721,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2031454541","https://openalex.org/W2594507094","https://openalex.org/W2883363148","https://openalex.org/W2895077992","https://openalex.org/W2895451584","https://openalex.org/W2896540732","https://openalex.org/W2963681621","https://openalex.org/W2990075400","https://openalex.org/W3097096317","https://openalex.org/W3190577943","https://openalex.org/W4234552385","https://openalex.org/W6600109629","https://openalex.org/W6620707391","https://openalex.org/W6683411478","https://openalex.org/W6684392942","https://openalex.org/W6722946945","https://openalex.org/W6743731764","https://openalex.org/W6753412334","https://openalex.org/W6785652829"],"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/W2913302899","https://openalex.org/W2922437833","https://openalex.org/W4223892596","https://openalex.org/W4312696271","https://openalex.org/W2933098581"],"abstract_inverted_index":{"Scale":[0],"variation":[1],"of":[2,60,64,72,110,114],"pedestrian":[3,10,17,23,38,115,145],"targets":[4,24],"is":[5,54],"a":[6,36,49,93],"major":[7],"challenge":[8],"in":[9,98],"detection,":[11],"which":[12],"leads":[13],"to":[14,20,56,79,101,125,134],"difficulties":[15],"for":[16,86,131],"detection":[18,39,136,146],"algorithms":[19],"accurately":[21],"capture":[22],"at":[25],"different":[26,73],"scales.":[27],"To":[28],"address":[29],"the":[30,58,69,81,99,108,120,157],"above":[31],"problems,":[32],"this":[33],"paper":[34],"proposes":[35],"multi-scale":[37],"method":[40,151],"based":[41],"on":[42,141],"attention":[43,96],"mechanism":[44,97],"and":[45,106,143],"feature":[46,51,70,83,112],"fusion.":[47],"First,":[48],"new":[50],"fusion":[52],"module":[53],"constructed":[55],"improve":[57,135],"problem":[59],"insufficient":[61],"semantic":[62],"information":[63,71,105,113],"shallow":[65],"features,":[66],"so":[67],"that":[68,149],"scales":[74],"can":[75],"be":[76],"fully":[77],"fused":[78],"strengthen":[80],"detector's":[82],"extraction":[84,109],"ability":[85],"small-scale":[87],"target":[88],"pedestrians.":[89],"Second,":[90],"we":[91,118],"introduce":[92],"spatial":[94],"channel":[95],"network":[100],"suppress":[102],"irrelevant":[103],"background":[104],"enhance":[107],"key":[111],"targets.":[116],"Finally,":[117],"optimize":[119],"original":[121],"prior":[122,129],"box":[123],"parameters":[124],"generate":[126],"more":[127],"suitable":[128],"boxes":[130],"detecting":[132],"pedestrians":[133],"accuracy.":[137],"Comparison":[138],"experiment":[139],"results":[140],"Caltech-USA":[142],"CityPersons":[144],"datasets":[147],"show":[148],"our":[150],"achieves":[152],"very":[153],"competitive":[154],"performance":[155],"with":[156],"state-of-the-art":[158],"methods.":[159]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}