{"id":"https://openalex.org/W2084506286","doi":"https://doi.org/10.1109/ivs.2012.6232226","title":"Real-time detection and tracking of pedestrians at intersections using a network of laserscanners","display_name":"Real-time detection and tracking of pedestrians at intersections using a network of laserscanners","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2084506286","doi":"https://doi.org/10.1109/ivs.2012.6232226","mag":"2084506286"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2012.6232226","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2012.6232226","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Intelligent Vehicles Symposium","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/A5110289998","display_name":"Daniel Mei\u00dfner","orcid":null},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Daniel Meissner","raw_affiliation_strings":["Institute of Measurement, Control, and Microtechnology, Ulm University, Ulm, Germany","[Institute of Measurement, Control and Microtechnology Ulm University, Ulm, Germany]"],"affiliations":[{"raw_affiliation_string":"Institute of Measurement, Control, and Microtechnology, Ulm University, Ulm, Germany","institution_ids":["https://openalex.org/I196349391"]},{"raw_affiliation_string":"[Institute of Measurement, Control and Microtechnology Ulm University, Ulm, Germany]","institution_ids":["https://openalex.org/I196349391"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014956076","display_name":"Stephan Reuter","orcid":"https://orcid.org/0000-0002-4858-1081"},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Stephan Reuter","raw_affiliation_strings":["Institute of Measurement, Control, and Microtechnology, Ulm University, Ulm, Germany","[Institute of Measurement, Control and Microtechnology Ulm University, Ulm, Germany]"],"affiliations":[{"raw_affiliation_string":"Institute of Measurement, Control, and Microtechnology, Ulm University, Ulm, Germany","institution_ids":["https://openalex.org/I196349391"]},{"raw_affiliation_string":"[Institute of Measurement, Control and Microtechnology Ulm University, Ulm, Germany]","institution_ids":["https://openalex.org/I196349391"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085054529","display_name":"Klaus Dietmayer","orcid":"https://orcid.org/0000-0002-1651-014X"},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Klaus Dietmayer","raw_affiliation_strings":["Institute of Measurement, Control, and Microtechnology, Ulm University, Ulm, Germany","[Institute of Measurement, Control and Microtechnology Ulm University, Ulm, Germany]"],"affiliations":[{"raw_affiliation_string":"Institute of Measurement, Control, and Microtechnology, Ulm University, Ulm, Germany","institution_ids":["https://openalex.org/I196349391"]},{"raw_affiliation_string":"[Institute of Measurement, Control and Microtechnology Ulm University, Ulm, Germany]","institution_ids":["https://openalex.org/I196349391"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5110289998"],"corresponding_institution_ids":["https://openalex.org/I196349391"],"apc_list":null,"apc_paid":null,"fwci":1.9218,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.87889414,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"1","issue":null,"first_page":"630","last_page":"635"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9962000250816345,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9962000250816345,"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/intersection","display_name":"Intersection (aeronautics)","score":0.7440654635429382},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7125331163406372},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6251163482666016},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6045872569084167},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5635829567909241},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5608974695205688},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5064424276351929},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4501684308052063},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.4488089084625244},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.43821507692337036},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.43324393033981323},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4212809205055237},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.41320914030075073},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.3205568194389343},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.26990461349487305},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.15842998027801514},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1287352740764618}],"concepts":[{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.7440654635429382},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7125331163406372},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6251163482666016},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6045872569084167},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5635829567909241},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5608974695205688},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5064424276351929},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4501684308052063},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.4488089084625244},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.43821507692337036},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.43324393033981323},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4212809205055237},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.41320914030075073},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3205568194389343},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.26990461349487305},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.15842998027801514},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1287352740764618},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2012.6232226","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2012.6232226","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Intelligent Vehicles Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5699999928474426,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W34992941","https://openalex.org/W1506806321","https://openalex.org/W1557595030","https://openalex.org/W1573546770","https://openalex.org/W1673310716","https://openalex.org/W2099159755","https://openalex.org/W2102625004","https://openalex.org/W2126885789","https://openalex.org/W2156950770","https://openalex.org/W2169248202","https://openalex.org/W2783270066","https://openalex.org/W3142704896","https://openalex.org/W6633511732","https://openalex.org/W6634202927","https://openalex.org/W6637131181","https://openalex.org/W6675141817","https://openalex.org/W6747617976"],"related_works":["https://openalex.org/W3016928466","https://openalex.org/W4389574804","https://openalex.org/W2348909947","https://openalex.org/W4292672442","https://openalex.org/W2015530857","https://openalex.org/W2362101859","https://openalex.org/W1994458110","https://openalex.org/W2100525497","https://openalex.org/W2965594636","https://openalex.org/W2912550626"],"abstract_inverted_index":{"Accident":[0],"analysis":[1],"shows":[2],"that":[3,29],"the":[4,34,58,67,75,81,96,105,107,117,120,140,154,157,169,174,186,193,203],"majority":[5],"of":[6,20,33,40,63,74,80,98,110,119,136,156,173],"accidents":[7,23],"with":[8,183],"body":[9],"injuries":[10],"occur":[11],"in":[12],"urban":[13,22],"areas":[14],"and":[15,56,69,86,101,123,181,206],"more":[16],"than":[17],"50":[18],"percent":[19],"those":[21],"happen":[24],"at":[25,48,190],"intersections.":[26],"Due":[27],"to":[28,45,94,147,150,167,201],"a":[30,61,71,84,159],"major":[31],"aim":[32],"Ko-PER":[35,187],"project,":[36],"which":[37,138],"is":[38,44,92,165,179,199],"part":[39],"research":[41],"initiative":[42],"Ko-FAS,":[43],"improve":[46],"safety":[47],"intersections":[49],"by":[50],"infrastructure":[51],"based":[52,115,176],"perception.":[53],"To":[54],"recognize":[55],"track":[57,168],"moving":[59],"objects,":[60],"network":[62],"laserscanner":[64,175],"sensors":[65],"observes":[66],"intersection":[68,189],"provides":[70],"3D":[72,82],"profile":[73],"current":[76],"scene.":[77],"By":[78],"means":[79],"measurements":[83,97,184],"robust":[85],"adaptive":[87],"Gaussian":[88],"mixture":[89],"background":[90],"model":[91],"trained":[93],"segment":[95],"dynamic":[99],"objects":[100],"static":[102],"objects.":[103],"After":[104],"segmentation,":[106],"foreground":[108],"points":[109],"each":[111],"sensor":[112],"are":[113,126,139],"clustered":[114],"on":[116,134],"density":[118],"point":[121],"clouds":[122],"finally":[124],"pedestrians":[125],"classified":[127],"using":[128],"dimension":[129],"features.":[130],"This":[131],"paper":[132],"focuses":[133],"tracking":[135,177,207],"pedestrians,":[137,158],"most":[141],"vulnerable":[142],"road":[143],"users.":[144],"In":[145],"order":[146],"be":[148],"able":[149],"integrate":[151],"dependencies":[152],"between":[153],"states":[155],"random":[160],"finite":[161],"set":[162],"particle":[163],"filter":[164],"used":[166,200],"pedestrians.":[170],"The":[171],"performance":[172],"system":[178],"shown":[180],"evaluated":[182],"from":[185],"test":[188],"Conti-Safety-Park.":[191],"Therefore,":[192],"optimal":[194],"subpattern":[195],"assignment":[196],"(OSPA)":[197],"metric":[198],"evaluate":[202],"object":[204],"recognition":[205],"system.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
