{"id":"https://openalex.org/W3047252832","doi":"https://doi.org/10.3390/s20164363","title":"Tuning of Classifiers to Speed-Up Detection of Pedestrians in Infrared Images","display_name":"Tuning of Classifiers to Speed-Up Detection of Pedestrians in Infrared Images","publication_year":2020,"publication_date":"2020-08-05","ids":{"openalex":"https://openalex.org/W3047252832","doi":"https://doi.org/10.3390/s20164363","mag":"3047252832","pmid":"https://pubmed.ncbi.nlm.nih.gov/32764301"},"language":"en","primary_location":{"id":"doi:10.3390/s20164363","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20164363","pdf_url":"https://www.mdpi.com/1424-8220/20/16/4363/pdf?version=1596618702","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/20/16/4363/pdf?version=1596618702","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009175649","display_name":"Karol Piniarski","orcid":"https://orcid.org/0000-0001-9329-720X"},"institutions":[{"id":"https://openalex.org/I46597724","display_name":"Pozna\u0144 University of Technology","ror":"https://ror.org/00p7p3302","country_code":"PL","type":"education","lineage":["https://openalex.org/I46597724"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Karol Piniarski","raw_affiliation_strings":["Division of Signal Processing and Electronic Systems, Institute of Automation and Robotics, Poznan University of Technology, Jana Paw\u0142a 24, 60-965 Pozna\u0144, Poland"],"raw_orcid":"https://orcid.org/0000-0001-9329-720X","affiliations":[{"raw_affiliation_string":"Division of Signal Processing and Electronic Systems, Institute of Automation and Robotics, Poznan University of Technology, Jana Paw\u0142a 24, 60-965 Pozna\u0144, Poland","institution_ids":["https://openalex.org/I46597724"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010451414","display_name":"Pawe\u0142 Paw\u0142owski","orcid":"https://orcid.org/0000-0001-5373-5148"},"institutions":[{"id":"https://openalex.org/I46597724","display_name":"Pozna\u0144 University of Technology","ror":"https://ror.org/00p7p3302","country_code":"PL","type":"education","lineage":["https://openalex.org/I46597724"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Pawe\u0142 Paw\u0142owski","raw_affiliation_strings":["Division of Signal Processing and Electronic Systems, Institute of Automation and Robotics, Poznan University of Technology, Jana Paw\u0142a 24, 60-965 Pozna\u0144, Poland"],"raw_orcid":"https://orcid.org/0000-0001-5373-5148","affiliations":[{"raw_affiliation_string":"Division of Signal Processing and Electronic Systems, Institute of Automation and Robotics, Poznan University of Technology, Jana Paw\u0142a 24, 60-965 Pozna\u0144, Poland","institution_ids":["https://openalex.org/I46597724"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101619455","display_name":"Adam D\u0105browski","orcid":"https://orcid.org/0000-0002-9385-6080"},"institutions":[{"id":"https://openalex.org/I46597724","display_name":"Pozna\u0144 University of Technology","ror":"https://ror.org/00p7p3302","country_code":"PL","type":"education","lineage":["https://openalex.org/I46597724"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Adam D\u0105browski","raw_affiliation_strings":["Division of Signal Processing and Electronic Systems, Institute of Automation and Robotics, Poznan University of Technology, Jana Paw\u0142a 24, 60-965 Pozna\u0144, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Signal Processing and Electronic Systems, Institute of Automation and Robotics, Poznan University of Technology, Jana Paw\u0142a 24, 60-965 Pozna\u0144, Poland","institution_ids":["https://openalex.org/I46597724"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5009175649"],"corresponding_institution_ids":["https://openalex.org/I46597724"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":0.3833,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.61042573,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"20","issue":"16","first_page":"4363","last_page":"4363"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9995999932289124,"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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.996999979019165,"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":0.9958000183105469,"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/pedestrian-detection","display_name":"Pedestrian detection","score":0.7837920188903809},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7547812461853027},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7372071146965027},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7351706624031067},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7109792232513428},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7008880376815796},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.5275330543518066},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5169074535369873},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5037896037101746},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.4924860894680023},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43378469347953796},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38381046056747437},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.3049274981021881},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1007058322429657}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7837920188903809},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7547812461853027},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7372071146965027},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7351706624031067},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7109792232513428},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7008880376815796},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.5275330543518066},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5169074535369873},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5037896037101746},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.4924860894680023},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43378469347953796},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38381046056747437},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.3049274981021881},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1007058322429657},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/s20164363","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20164363","pdf_url":"https://www.mdpi.com/1424-8220/20/16/4363/pdf?version=1596618702","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:32764301","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32764301","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:62e37c22815b47b1aa0926e3741e1d7f","is_oa":true,"landing_page_url":"https://doaj.org/article/62e37c22815b47b1aa0926e3741e1d7f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 20, Iss 16, p 4363 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/20/16/4363/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s20164363","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 20; Issue 16; Pages: 4363","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7471988","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7471988","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s20164363","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20164363","pdf_url":"https://www.mdpi.com/1424-8220/20/16/4363/pdf?version=1596618702","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6100000143051147}],"awards":[{"id":"https://openalex.org/G1845117233","display_name":null,"funder_award_id":"872614","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"}],"funders":[{"id":"https://openalex.org/F4320332999","display_name":"Horizon 2020 Framework Programme","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3047252832.pdf","grobid_xml":"https://content.openalex.org/works/W3047252832.grobid-xml"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1236338176","https://openalex.org/W1488500354","https://openalex.org/W1591749664","https://openalex.org/W1598330724","https://openalex.org/W1663973292","https://openalex.org/W1965484410","https://openalex.org/W1989249224","https://openalex.org/W2010452185","https://openalex.org/W2013133194","https://openalex.org/W2034865129","https://openalex.org/W2049770187","https://openalex.org/W2074777933","https://openalex.org/W2112385363","https://openalex.org/W2120339771","https://openalex.org/W2125556102","https://openalex.org/W2126674031","https://openalex.org/W2129151427","https://openalex.org/W2129317335","https://openalex.org/W2153635508","https://openalex.org/W2155893237","https://openalex.org/W2156546852","https://openalex.org/W2156909104","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2194775991","https://openalex.org/W2213018794","https://openalex.org/W2265127172","https://openalex.org/W2492464543","https://openalex.org/W2501994121","https://openalex.org/W2537924573","https://openalex.org/W2560621248","https://openalex.org/W2610165754","https://openalex.org/W2613296094","https://openalex.org/W2618530766","https://openalex.org/W2621349708","https://openalex.org/W2766105441","https://openalex.org/W2781581716","https://openalex.org/W2790057412","https://openalex.org/W2792925809","https://openalex.org/W2796688730","https://openalex.org/W2801942697","https://openalex.org/W2884771495","https://openalex.org/W2889480448","https://openalex.org/W2915823913","https://openalex.org/W2935568451","https://openalex.org/W2944689333","https://openalex.org/W2950561226","https://openalex.org/W2952707767","https://openalex.org/W2970073937","https://openalex.org/W3021471359","https://openalex.org/W3097096317","https://openalex.org/W4230674625","https://openalex.org/W6629510986","https://openalex.org/W6679075930","https://openalex.org/W6683411478"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W4231704780","https://openalex.org/W2512789322","https://openalex.org/W2083794993","https://openalex.org/W352609212","https://openalex.org/W3122828758","https://openalex.org/W4404692318","https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W3204901196"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"an":[3],"experimental":[4],"evaluation":[5],"of":[6,26,37,93,106,127],"real-time":[7],"pedestrian":[8],"detection":[9,105],"algorithms":[10],"and":[11,75,86,118],"their":[12],"tuning":[13],"using":[14],"the":[15,23,34,38,46,55,59,72,76,94],"proposed":[16,95],"universal":[17],"performance":[18],"index.":[19],"With":[20],"this":[21],"index,":[22],"precise":[24],"choice":[25],"various":[27,83],"parameters":[28],"is":[29,42],"possible.":[30],"Moreover,":[31],"we":[32,52,69],"determined":[33],"best":[35],"resolution":[36],"analysis":[39],"window,":[40],"which":[41,68],"much":[43],"lower":[44],"than":[45],"initial":[47],"window.":[48],"By":[49],"such":[50],"means,":[51],"can":[53],"speed-up":[54],"processing":[56,73],"(i.e.,":[57],"reduce":[58],"classification":[60,77],"time":[61],"by":[62],"74%).":[63],"There":[64],"are":[65,100],"cases":[66],"in":[67,88],"increased":[70],"both":[71],"speed":[74],"accuracy.":[78],"We":[79,123],"made":[80],"experiments":[81],"with":[82],"baseline":[84],"detectors":[85],"datasets":[87],"order":[89],"to":[90,104],"confirm":[91],"versatility":[92],"ideas.":[96],"The":[97],"analyzed":[98],"classifiers":[99],"those":[101],"typically":[102],"applied":[103],"pedestrians,":[107],"namely:":[108],"aggregated":[109],"channel":[110],"feature":[111],"(ACF),":[112],"deep":[113],"convolutional":[114],"neural":[115],"network":[116],"(CNN),":[117],"support":[119],"vector":[120],"machine":[121],"(SVM).":[122],"used":[124],"a":[125],"suite":[126],"five":[128],"precisely":[129],"chosen":[130],"night":[131],"(and":[132],"day)":[133],"IR":[134],"vision":[135],"datasets.":[136]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}