{"id":"https://openalex.org/W1598330724","doi":"https://doi.org/10.1109/mva.2015.7153177","title":"Pedestrian detection in thermal images using adaptive fuzzy C-means clustering and convolutional neural networks","display_name":"Pedestrian detection in thermal images using adaptive fuzzy C-means clustering and convolutional neural networks","publication_year":2015,"publication_date":"2015-05-01","ids":{"openalex":"https://openalex.org/W1598330724","doi":"https://doi.org/10.1109/mva.2015.7153177","mag":"1598330724"},"language":"en","primary_location":{"id":"doi:10.1109/mva.2015.7153177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mva.2015.7153177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 14th IAPR International Conference on Machine Vision Applications (MVA)","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/A5007962043","display_name":"Vijay John","orcid":"https://orcid.org/0000-0002-9553-0906"},"institutions":[{"id":"https://openalex.org/I4840577","display_name":"Toyota Technological Institute","ror":"https://ror.org/001hv0k59","country_code":"JP","type":"education","lineage":["https://openalex.org/I4840577"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Vijay John","raw_affiliation_strings":["Toyota Technological Institute, Japan"],"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute, Japan","institution_ids":["https://openalex.org/I4840577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112282385","display_name":"Seiichi Mita","orcid":null},"institutions":[{"id":"https://openalex.org/I4840577","display_name":"Toyota Technological Institute","ror":"https://ror.org/001hv0k59","country_code":"JP","type":"education","lineage":["https://openalex.org/I4840577"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Seiichi Mita","raw_affiliation_strings":["Toyota Technological Institute, Japan"],"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute, Japan","institution_ids":["https://openalex.org/I4840577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100655628","display_name":"Zheng Liu","orcid":"https://orcid.org/0009-0009-0821-6176"},"institutions":[{"id":"https://openalex.org/I4840577","display_name":"Toyota Technological Institute","ror":"https://ror.org/001hv0k59","country_code":"JP","type":"education","lineage":["https://openalex.org/I4840577"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zheng Liu","raw_affiliation_strings":["Toyota Technological Institute, Japan"],"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute, Japan","institution_ids":["https://openalex.org/I4840577"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071461756","display_name":"Bin Qi","orcid":"https://orcid.org/0000-0001-9250-8976"},"institutions":[{"id":"https://openalex.org/I4840577","display_name":"Toyota Technological Institute","ror":"https://ror.org/001hv0k59","country_code":"JP","type":"education","lineage":["https://openalex.org/I4840577"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Bin Qi","raw_affiliation_strings":["Toyota Technological Institute, Japan"],"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute, Japan","institution_ids":["https://openalex.org/I4840577"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5007962043"],"corresponding_institution_ids":["https://openalex.org/I4840577"],"apc_list":null,"apc_paid":null,"fwci":6.9956,"has_fulltext":false,"cited_by_count":116,"citation_normalized_percentile":{"value":0.97951492,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"246","last_page":"249"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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":0.7708344459533691},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7566177248954773},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.741675853729248},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.6129750609397888},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5740892887115479},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5694109797477722},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.51948082447052},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4967718720436096},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4841392934322357},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4463118314743042},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.28339892625808716},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09932711720466614}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7708344459533691},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7566177248954773},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.741675853729248},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.6129750609397888},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5740892887115479},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5694109797477722},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.51948082447052},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4967718720436096},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4841392934322357},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4463118314743042},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.28339892625808716},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09932711720466614},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mva.2015.7153177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mva.2015.7153177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 14th IAPR International Conference on Machine Vision Applications (MVA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1519391713","https://openalex.org/W1544640522","https://openalex.org/W2005181468","https://openalex.org/W2031454541","https://openalex.org/W2066740411","https://openalex.org/W2089001157","https://openalex.org/W2111227709","https://openalex.org/W2130712638","https://openalex.org/W2150764772","https://openalex.org/W2155893237","https://openalex.org/W2160127501","https://openalex.org/W2163605009","https://openalex.org/W6631128323","https://openalex.org/W6679036608","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2802018156","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W2969228573","https://openalex.org/W2913302899","https://openalex.org/W2963690996","https://openalex.org/W4223892596","https://openalex.org/W4312696271","https://openalex.org/W2933098581","https://openalex.org/W2556125083"],"abstract_inverted_index":{"Pedestrian":[0],"detection":[1,34,73,114,199],"is":[2,35,55,100,118,137,169,187,205],"paramount":[3],"for":[4,37,46,58,112],"advanced":[5],"driver":[6],"assistance":[7],"systems":[8],"(ADAS)":[9],"and":[10,29,126,144,159,176],"autonomous":[11],"driving.":[12],"As":[13],"a":[14],"key":[15],"technology":[16],"in":[17,39],"computer":[18],"vision,":[19],"it":[20],"also":[21],"finds":[22],"many":[23],"other":[24],"applications,":[25],"such":[26],"as":[27],"security":[28],"surveillance":[30],"etc.":[31],"Generally,":[32],"pedestrian":[33,113],"conducted":[36],"images":[38,143],"visible":[40],"spectrum,":[41],"which":[42],"are":[43,87,130,152],"not":[44],"suitable":[45],"night":[47,59],"time":[48,60],"detection.":[49],"Infrared":[50],"(IR)":[51],"or":[52],"thermal":[53],"imaging":[54],"often":[56],"adopted":[57],"due":[61],"to":[62,89,139,171],"its":[63],"capability":[64],"of":[65,96,183],"capturing":[66],"the":[67,80,97,141,146,160,178,184],"emitted":[68],"energy":[69],"from":[70,79,115],"pedestrians.":[71,148],"The":[72,132,149,165,181],"process":[74],"firstly":[75],"extracts":[76],"candidate":[77,147,150],"pedestrians":[78,151],"captured":[81],"IR":[82,116,142],"image.":[83],"Robust":[84],"feature":[85],"descriptors":[86],"formulated":[88],"represent":[90],"those":[91],"candidates.":[92],"A":[93,197],"binary":[94,179],"classification":[95],"extract":[98],"features":[99,175],"then":[101,153],"performed":[102],"with":[103,189,201],"trained":[104],"classifier":[105],"models.":[106],"In":[107],"this":[108],"paper,":[109],"an":[110,121],"algorithm":[111,186],"image":[117],"proposed,":[119],"where":[120],"adaptive":[122,133],"fuzzy":[123,134],"C-means":[124,135],"clustering":[125,136],"convolutional":[127,166],"neural":[128,167],"networks":[129],"adopted.":[131],"used":[138,170],"segment":[140],"retrieve":[145],"pruned":[154],"using":[155],"human":[156],"posture":[157],"characteristics":[158],"second":[161],"central":[162],"moments":[163],"ellipse.":[164],"network":[168],"simultaneously":[172],"learn":[173],"relevant":[174],"perform":[177],"classification.":[180],"performance":[182],"proposed":[185],"compared":[188],"state-of-the-art":[190],"algorithms":[191],"on":[192],"publicly":[193],"available":[194],"data":[195],"set.":[196],"better":[198],"accuracy":[200,204],"reduced":[202],"computational":[203],"achieved.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":24},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":21},{"year":2018,"cited_by_count":20},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}