{"id":"https://openalex.org/W2896115433","doi":"https://doi.org/10.1109/ivs.2018.8500453","title":"Evaluation of Synthetic Video Data in Machine Learning Approaches for Parking Space Classification","display_name":"Evaluation of Synthetic Video Data in Machine Learning Approaches for Parking Space Classification","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2896115433","doi":"https://doi.org/10.1109/ivs.2018.8500453","mag":"2896115433"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2018.8500453","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2018.8500453","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Intelligent Vehicles Symposium (IV)","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/A5048705128","display_name":"Daniela Horn","orcid":"https://orcid.org/0000-0002-4337-0672"},"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":true,"raw_author_name":"Daniela Horn","raw_affiliation_strings":["University of Bochum, Institute for Neural Computation"],"affiliations":[{"raw_affiliation_string":"University of Bochum, Institute for Neural Computation","institution_ids":["https://openalex.org/I904495901"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020225503","display_name":"Sebastian Houben","orcid":"https://orcid.org/0000-0002-2036-419X"},"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":"Sebastian Houben","raw_affiliation_strings":["University of Bochum, Institute for Neural Computation"],"affiliations":[{"raw_affiliation_string":"University of Bochum, Institute for Neural Computation","institution_ids":["https://openalex.org/I904495901"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5048705128"],"corresponding_institution_ids":["https://openalex.org/I904495901"],"apc_list":null,"apc_paid":null,"fwci":0.2089,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.55758483,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2157","last_page":"2162"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9990000128746033,"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":0.9990000128746033,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9990000128746033,"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/T12546","display_name":"Smart Parking Systems Research","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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":0.7921781539916992},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7173026204109192},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6412964463233948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5892704725265503},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.47210589051246643},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44068434834480286},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43648576736450195},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08432275056838989}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7921781539916992},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7173026204109192},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6412964463233948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5892704725265503},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.47210589051246643},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44068434834480286},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43648576736450195},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08432275056838989},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2018.8500453","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2018.8500453","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1656228339","https://openalex.org/W1663973292","https://openalex.org/W1931767201","https://openalex.org/W1974507096","https://openalex.org/W1994541145","https://openalex.org/W2002427601","https://openalex.org/W2031454541","https://openalex.org/W2100309510","https://openalex.org/W2319050173","https://openalex.org/W2427939170","https://openalex.org/W2594639291","https://openalex.org/W2740823390"],"related_works":["https://openalex.org/W3147584709","https://openalex.org/W2961085424","https://openalex.org/W2977677679","https://openalex.org/W1992327129","https://openalex.org/W2381986121","https://openalex.org/W2370918718","https://openalex.org/W4224009465","https://openalex.org/W2256933480","https://openalex.org/W4306674287","https://openalex.org/W3098003361"],"abstract_inverted_index":{"Most":[0],"modern":[1],"computer":[2],"vision":[3],"techniques":[4],"rely":[5,139],"on":[6,62,140,180,187,206],"large":[7],"amounts":[8],"of":[9,47,58,65,75,91,126,177,191],"meticulously":[10],"annotated":[11],"data":[12,48,76,104,132,152,162],"for":[13,50,134,151],"training":[14,161],"and":[15,36,68,128,154,159,184],"evaluation.":[16],"In":[17,110],"close-to-market":[18],"development,":[19],"this":[20,111,207],"demand":[21],"is":[22,55],"even":[23,106],"higher":[24],"since":[25],"numerous":[26],"common":[27,30,175],"and-more":[28],"important-less":[29],"situations":[31],"have":[32],"to":[33,86,122],"be":[34,39,78],"tested":[35],"must":[37],"hence":[38],"covered":[40],"datawise.":[41],"However,":[42],"gathering":[43],"the":[44,51,63,66,69,72,81,84,124],"necessary":[45],"amount":[46,74],"ready-labeled":[49],"task":[52,208],"at":[53,108],"hand":[54],"a":[56,92,141,174,198],"challenge":[57],"its":[59],"own.":[60],"Depending":[61],"complexity":[64],"objective":[67],"chosen":[70],"approach,":[71],"required":[73],"can":[77],"vast.":[79],"At":[80],"same":[82],"time,":[83],"effort":[85],"capture":[87],"all":[88,156,211],"possible":[89],"cases":[90],"given":[93],"problem":[94],"grows":[95],"with":[96,147,173,216],"their":[97],"variability.":[98],"This":[99],"makes":[100],"recording":[101],"new":[102],"video":[103,166,218],"unfeasible,":[105],"impossible":[107],"times.":[109],"work,":[112],"we":[113],"regard":[114],"parking":[115],"space":[116],"classification":[117,199],"as":[118],"an":[119],"exemplary":[120],"application":[121],"target":[123],"imbalance":[125],"cost":[127],"benefit":[129],"w.r.t.":[130],"image":[131],"creation":[133,153],"machine":[135],"learning":[136],"approaches.":[137],"We":[138,168,196],"fully-fledged":[142],"park":[143],"deck":[144],"simulation":[145],"created":[146],"Unreal":[148],"Engine":[149],"4":[150],"replace":[155],"conventionally":[157],"recorded":[158],"hand-labeled":[160],"by":[163],"automatically-annotated":[164],"synthetic":[165,181],"data.":[167,219],"train":[169],"several":[170],"of-the-shelf":[171],"classifiers":[172,212],"choice":[176],"feature":[178],"inputs":[179],"images":[182],"only":[183],"evaluate":[185],"them":[186],"two":[188],"realworld":[189],"sequences":[190],"different":[192],"outdoor":[193],"car":[194],"parks.":[195],"reach":[197],"performance":[200],"that":[201],"matches":[202],"our":[203],"previous":[204],"work":[205],"in":[209],"which":[210],"were":[213],"developed":[214],"solely":[215],"real-life":[217]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
