{"id":"https://openalex.org/W2411990577","doi":"https://doi.org/10.1109/acpr.2015.7486527","title":"Efficient multiclass object detection: Detecting pedestrians and bicyclists in a truck's blind spot camera","display_name":"Efficient multiclass object detection: Detecting pedestrians and bicyclists in a truck's blind spot camera","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2411990577","doi":"https://doi.org/10.1109/acpr.2015.7486527","mag":"2411990577"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2015.7486527","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486527","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://lirias.kuleuven.be/handle/123456789/515413","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085816625","display_name":"Kristof Van Beeck","orcid":"https://orcid.org/0000-0002-3667-7406"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Kristof Van Beeck","raw_affiliation_strings":["Technology Campus De Nayer, KU Leuven, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technology Campus De Nayer, KU Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057957125","display_name":"Toon Goedem\u00e9","orcid":"https://orcid.org/0000-0002-7477-8961"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Toon Goedeme","raw_affiliation_strings":["Technology Campus De Nayer, KU Leuven, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technology Campus De Nayer, KU Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99464096"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"366","last_page":"370"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9973999857902527,"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":0.9907000064849854,"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":0.7986519932746887},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7262696027755737},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.705203652381897},{"id":"https://openalex.org/keywords/blind-spot","display_name":"Blind spot","score":0.7037461400032043},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6223254799842834},{"id":"https://openalex.org/keywords/image-warping","display_name":"Image warping","score":0.5833234190940857},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.4572955369949341},{"id":"https://openalex.org/keywords/truck","display_name":"Truck","score":0.45146727561950684},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4271260201931},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.22318139672279358},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10383322834968567}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7986519932746887},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7262696027755737},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.705203652381897},{"id":"https://openalex.org/C64731932","wikidata":"https://www.wikidata.org/wiki/Q371090","display_name":"Blind spot","level":2,"score":0.7037461400032043},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6223254799842834},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.5833234190940857},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.4572955369949341},{"id":"https://openalex.org/C52121051","wikidata":"https://www.wikidata.org/wiki/Q43193","display_name":"Truck","level":2,"score":0.45146727561950684},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4271260201931},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.22318139672279358},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10383322834968567},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/acpr.2015.7486527","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486527","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:lirias2repo.kuleuven.be:123456789/515413","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/123456789/515413","pdf_url":null,"source":{"id":"https://openalex.org/S7407055369","display_name":"Lirias","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, Malaysia, 3-6 November 2015","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:lirias2repo.kuleuven.be:123456789/515413","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/123456789/515413","pdf_url":null,"source":{"id":"https://openalex.org/S7407055369","display_name":"Lirias","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, Malaysia, 3-6 November 2015","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6800000071525574,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1650122911","https://openalex.org/W2031454541","https://openalex.org/W2036989445","https://openalex.org/W2077513643","https://openalex.org/W2084568123","https://openalex.org/W2086312100","https://openalex.org/W2102605133","https://openalex.org/W2117539524","https://openalex.org/W2120419212","https://openalex.org/W2125556102","https://openalex.org/W2133755669","https://openalex.org/W2136724559","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2395551311","https://openalex.org/W2951638509","https://openalex.org/W2952020226","https://openalex.org/W6636787326","https://openalex.org/W6675026286","https://openalex.org/W6677651945","https://openalex.org/W6679911680"],"related_works":["https://openalex.org/W1670332068","https://openalex.org/W2095618524","https://openalex.org/W3018571661","https://openalex.org/W2735770592","https://openalex.org/W1971024059","https://openalex.org/W1532661003","https://openalex.org/W2380251249","https://openalex.org/W2001283968","https://openalex.org/W2126960140","https://openalex.org/W2371038066"],"abstract_inverted_index":{"In":[0,107],"this":[1,34,108,132],"paper":[2],"we":[3,38,80,89,110,133,147,158],"propose":[4,134],"an":[5,42],"efficient":[6,119],"detection":[7,114,120],"and":[8,69,77,87,98,124],"tracking":[9],"framework":[10,157],"targeting":[11],"vulnerable":[12,62],"road":[13,63],"users":[14,64],"in":[15,126,150],"the":[16,49,53,94,103,118,135],"blind":[17,54,104,163,169],"spot":[18,55,105,164,170],"camera":[19,171],"images":[20],"of":[21,52,71,75,121,137],"a":[22,66,72,112,138,151,167,174],"truck.":[23,176],"Existing":[24],"non-vision":[25],"based":[26,46],"safety":[27,44],"solutions":[28],"are":[29,65],"not":[30],"able":[31],"to":[32,40,82,91],"handle":[33],"problem":[35],"completely.":[36],"Therefore":[37],"aim":[39],"develop":[41],"active":[43],"system,":[45],"solely":[47],"on":[48,173,185],"vision":[50],"input":[51],"camera.":[56,106],"This":[57],"is":[58],"far":[59],"from":[60],"trivial:":[61],"diverse":[67],"class":[68],"consist":[70],"wide":[73],"variety":[74],"poses":[76],"appearances.":[78],"Evidently":[79],"need":[81,90],"achieve":[83,131],"excellent":[84,183],"accuracy":[85,184],"results":[86],"furthermore":[88],"cope":[92],"with":[93,142,166],"large":[95],"lens":[96],"distortion":[97],"extreme":[99],"viewpoints":[100],"induced":[101],"by":[102],"work":[109],"present":[111],"multiclass":[113],"methodology":[115],"which":[116,146],"enables":[117],"both":[122],"pedestrians":[123],"bicyclists":[125],"these":[127,186],"challenging":[128,187],"images.":[129],"To":[130,154],"integration":[136],"warping":[139],"window":[140],"approach":[141,181],"multiple":[143],"object":[144],"detectors":[145],"intelligently":[148],"combine":[149],"probabilistic":[152],"manner.":[153],"validate":[155],"our":[156,180],"recorded":[159],"several":[160],"simulated":[161],"dangerous":[162],"scenarios":[165],"genuine":[168],"mounted":[172],"real":[175],"We":[177],"show":[178],"that":[179],"achieves":[182],"datasets.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}