{"id":"https://openalex.org/W1671917554","doi":"https://doi.org/10.1109/ijcnn.2015.7280499","title":"Extending traffic light recognition: Efficient classification of phase and pictogram","display_name":"Extending traffic light recognition: Efficient classification of phase and pictogram","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W1671917554","doi":"https://doi.org/10.1109/ijcnn.2015.7280499","mag":"1671917554"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2015.7280499","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2015.7280499","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Joint Conference on Neural Networks (IJCNN)","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/A5109858842","display_name":"Michael Matthias","orcid":null},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Matthias Michael","raw_affiliation_strings":["Institut flir Neuroinformatik, Ruhr-Universit\u00e4t Bochum","Institut f\u00fcr Neuroinformatik, Ruhr-Universit\u00e4t Bochum, Germany#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut flir Neuroinformatik, Ruhr-Universit\u00e4t Bochum","institution_ids":["https://openalex.org/I904495901"]},{"raw_affiliation_string":"Institut f\u00fcr Neuroinformatik, Ruhr-Universit\u00e4t Bochum, Germany#TAB#","institution_ids":["https://openalex.org/I904495901"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025991216","display_name":"Marc Schlipsing","orcid":null},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Marc Schlipsing","raw_affiliation_strings":["Institut flir Neuroinformatik, Ruhr-Universit\u00e4t Bochum","Institut f\u00fcr Neuroinformatik, Ruhr-Universit\u00e4t Bochum, Germany#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut flir Neuroinformatik, Ruhr-Universit\u00e4t Bochum","institution_ids":["https://openalex.org/I904495901"]},{"raw_affiliation_string":"Institut f\u00fcr Neuroinformatik, Ruhr-Universit\u00e4t Bochum, Germany#TAB#","institution_ids":["https://openalex.org/I904495901"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6849,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.89113671,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"9","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9970999956130981,"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":0.9970999956130981,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9936000108718872,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7616432905197144},{"id":"https://openalex.org/keywords/pictogram","display_name":"Pictogram","score":0.7203139066696167},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7100221514701843},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6831817626953125},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5977580547332764},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5321186184883118},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5206215381622314},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5196308493614197},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5054514408111572},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.43474695086479187},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.42515626549720764},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.41207748651504517},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.37327975034713745},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36052775382995605},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11215522885322571}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7616432905197144},{"id":"https://openalex.org/C7220189","wikidata":"https://www.wikidata.org/wiki/Q52827","display_name":"Pictogram","level":2,"score":0.7203139066696167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7100221514701843},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6831817626953125},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5977580547332764},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5321186184883118},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5206215381622314},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5196308493614197},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5054514408111572},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.43474695086479187},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.42515626549720764},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.41207748651504517},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.37327975034713745},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36052775382995605},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11215522885322571},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2015.7280499","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2015.7280499","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.711.2033","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.711.2033","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ini.rub.de/uploads/document/attachment/289/IJCNN2015_MichaelSchlipsing_TrafficLightsClassification.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1968029124","https://openalex.org/W1968974896","https://openalex.org/W1981562962","https://openalex.org/W1987650807","https://openalex.org/W2021383150","https://openalex.org/W2098368939","https://openalex.org/W2099355420","https://openalex.org/W2105529173","https://openalex.org/W2108950639","https://openalex.org/W2114281890","https://openalex.org/W2124158580","https://openalex.org/W2136891917","https://openalex.org/W2161969291","https://openalex.org/W2164623278","https://openalex.org/W2549975402","https://openalex.org/W3097096317","https://openalex.org/W6645807589","https://openalex.org/W6674902714","https://openalex.org/W6678649364","https://openalex.org/W6729089581"],"related_works":["https://openalex.org/W2565656575","https://openalex.org/W1975547468","https://openalex.org/W2804627982","https://openalex.org/W2001679188","https://openalex.org/W4205493345","https://openalex.org/W2196068029","https://openalex.org/W2016342027","https://openalex.org/W2888859519","https://openalex.org/W1538678705","https://openalex.org/W2310826128"],"abstract_inverted_index":{"While":[0],"much":[1],"work":[2],"in":[3,12,179],"the":[4,13,52,73,91,94,108,131,135,144,147],"domain":[5],"of":[6,15,19,45,96,110,122,134,149,161,166],"traffic":[7,16,181],"lights":[8,66,76],"recognition":[9,183],"is":[10,30,87],"invested":[11],"detection":[14],"lights,":[17],"classification":[18,44,132,159],"their":[20],"exact":[21],"state":[22,47],"(including":[23],"color":[24],"phase":[25,136],"and":[26,55,69,79,116,128,140],"possible":[27],"arrow":[28],"pictogram)":[29],"often":[31],"neglected.":[32],"In":[33,100],"this":[34],"paper,":[35],"we":[36,106],"propose":[37],"a":[38,82,164],"robust":[39],"approach":[40],"for":[41,143],"efficient":[42],"video-based":[43],"said":[46],"with":[48,93,146],"particular":[49],"attention":[50],"to":[51,59,71,89,102,156],"displayed":[53],"pictogram":[54,92],"an":[56,120,157],"additional":[57],"ability":[58],"reject":[60],"false":[61],"detections.":[62],"The":[63,75],"currently":[64],"active":[65],"are":[67,77],"identified":[68],"used":[70,88],"classify":[72,90],"phase.":[74],"extracted":[78],"transformed":[80],"into":[81],"HOG":[83],"feature":[84],"representation":[85],"that":[86,130],"help":[95],"machine":[97],"learning":[98],"classifiers.":[99],"order":[101],"gain":[103],"optimal":[104],"results,":[105],"compared":[107],"performance":[109],"different":[111],"algorithms,":[112],"namely":[113],"LDA,":[114],"kNN,":[115],"SVM.":[117],"We":[118],"provide":[119],"evaluation":[121],"our":[123,173],"method":[124],"on":[125],"individual":[126],"images":[127],"demonstrate":[129],"rate":[133],"lies":[137],"at":[138,141],"96.7%":[139],"92.8%":[142],"pictogram,":[145],"use":[148],"SVMs":[150],"providing":[151],"best":[152],"results.":[153],"This":[154],"leads":[155],"overall":[158],"quality":[160],"89.9%.":[162],"With":[163],"runtime":[165],"less":[167],"than":[168],"1ms":[169],"per":[170],"image":[171],"section":[172],"algorithm":[174],"can":[175],"easily":[176],"be":[177],"integrated":[178],"every":[180],"light":[182],"pipeline.":[184]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
