{"id":"https://openalex.org/W2978576364","doi":"https://doi.org/10.2352/issn.2470-1173.2018.10.imawm-375","title":"A feature fusion strategy for human detection in omnidirectional camera imagery","display_name":"A feature fusion strategy for human detection in omnidirectional camera imagery","publication_year":2018,"publication_date":"2018-01-28","ids":{"openalex":"https://openalex.org/W2978576364","doi":"https://doi.org/10.2352/issn.2470-1173.2018.10.imawm-375","mag":"2978576364"},"language":"en","primary_location":{"id":"doi:10.2352/issn.2470-1173.2018.10.imawm-375","is_oa":false,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2018.10.imawm-375","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","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/A5074918434","display_name":"Hussin K. Ragb","orcid":"https://orcid.org/0009-0007-4039-6512"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hussin K. Ragb","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5061050831","display_name":"Vijayan K. Asari","orcid":"https://orcid.org/0000-0002-3751-5492"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vijayan K. Asari","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.1002,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.97731767,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"30","issue":"10","first_page":"375","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9825999736785889,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9121999740600586,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/phase-congruency","display_name":"Phase congruency","score":0.8906834125518799},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8426370620727539},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7683926820755005},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6664869785308838},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5429527163505554},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5097195506095886},{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.4968881905078888},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.49485868215560913},{"id":"https://openalex.org/keywords/omnidirectional-camera","display_name":"Omnidirectional camera","score":0.4453054964542389},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.41117045283317566},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40083709359169006},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3296365737915039},{"id":"https://openalex.org/keywords/omnidirectional-antenna","display_name":"Omnidirectional antenna","score":0.3003354072570801},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22200146317481995},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19262433052062988}],"concepts":[{"id":"https://openalex.org/C2777316791","wikidata":"https://www.wikidata.org/wiki/Q17105246","display_name":"Phase congruency","level":3,"score":0.8906834125518799},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8426370620727539},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7683926820755005},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6664869785308838},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5429527163505554},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5097195506095886},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.4968881905078888},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.49485868215560913},{"id":"https://openalex.org/C2777953668","wikidata":"https://www.wikidata.org/wiki/Q684116","display_name":"Omnidirectional camera","level":4,"score":0.4453054964542389},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.41117045283317566},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40083709359169006},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3296365737915039},{"id":"https://openalex.org/C24027999","wikidata":"https://www.wikidata.org/wiki/Q2176348","display_name":"Omnidirectional antenna","level":3,"score":0.3003354072570801},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22200146317481995},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19262433052062988},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C21822782","wikidata":"https://www.wikidata.org/wiki/Q131214","display_name":"Antenna (radio)","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2352/issn.2470-1173.2018.10.imawm-375","is_oa":false,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2018.10.imawm-375","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4379536980","https://openalex.org/W2898919627","https://openalex.org/W2666300258","https://openalex.org/W1555939286","https://openalex.org/W2215517927","https://openalex.org/W1989062809","https://openalex.org/W2118689766","https://openalex.org/W2625795345","https://openalex.org/W2137384304","https://openalex.org/W2422277458"],"abstract_inverted_index":{"Field":[0],"of":[1,3,26,53,71,154,163,188,194,208,217],"view":[2],"the":[4,20,48,51,77,84,87,95,100,137,152,167,186,192,209,218,223,244],"traditional":[5],"camera":[6,34,69,155],"is":[7,16,170,212,249],"limited":[8],"such":[9],"that":[10,64],"usually":[11,29],"more":[12,31],"than":[13],"three":[14,224],"cameras":[15,28,74],"needed":[17],"to":[18,46,75,108,135,151,174,233],"cover":[19,76],"entire":[21,78],"surveillance":[22,79],"area.":[23],"The":[24,121,157,177,246],"use":[25,83],"multiple":[27,73],"requires":[30],"efforts":[32],"regarding":[33],"control":[35],"and":[36,99,103,117,124,161,185,191,203,222,241,256],"set":[37],"up":[38],"as":[39,41,113],"well":[40],"they":[42,104],"need":[43],"additional":[44],"algorithms":[45],"find":[47],"relationships":[49],"among":[50],"images":[52,179],"different":[54],"cameras.":[55],"In":[56],"this":[57],"paper,":[58],"we":[59,82],"present":[60],"a":[61,234,252],"multi-feature":[62],"algorithm":[63],"employs":[65],"only":[66],"one":[67,110,215],"omnidirectional":[68,254],"instead":[70],"using":[72],"region.":[80],"Here":[81],"image":[85,122,169],"gradients,":[86,123],"local":[88,125,183,201],"phase":[89,93,96,126,130,158,219],"information":[90,127],"based":[91,128],"on":[92,129,251],"congruency,":[94],"congruency":[97,131,159,220],"magnitude,":[98],"color":[101,226],"features,":[102],"are":[105,133,148,180,197,231],"fused":[106],"together":[107],"build":[109],"descriptor":[111],"named":[112],"\"Fused":[114],"Phase,":[115],"Gradients":[116],"Color":[118],"features":[119,147,211,230],"(FPGC).":[120],"concept":[132],"used":[134,149],"extract":[136],"human":[138],"body":[139],"shape":[140],"features.":[141],"Either":[142],"LUV":[143,225],"or":[144],"grayscale":[145],"channel":[146,216],"according":[150],"kind":[153],"used.":[156],"magnitude":[160,221],"orientation":[162],"each":[164,200],"pixel":[165],"in":[166],"input":[168],"computed":[171],"with":[172],"respect":[173],"its":[175],"neighborhood.":[176],"resultant":[178],"divided":[181],"into":[182],"regions":[184],"histogram":[187,193],"oriented":[189,195],"phase,":[190],"gradient":[196],"determined":[198],"for":[199,214,239],"region":[202],"combined.":[204],"A":[205],"maximum":[206],"pooling":[207],"candidate":[210],"generated":[213],"channels.":[227],"All":[228],"these":[229],"fed":[232],"decision":[235],"tree":[236],"Adaboost":[237],"classifier":[238],"training":[240],"classification":[242],"between":[243],"classes.":[245],"proposed":[247],"approach":[248],"evaluated":[250],"challenging":[253],"dataset":[255],"observed":[257],"promising":[258],"performance.":[259]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}
