{"id":"https://openalex.org/W2896704645","doi":"https://doi.org/10.1109/ijcnn.2018.8489289","title":"A temporal-based deep learning method for multiple objects detection in autonomous driving","display_name":"A temporal-based deep learning method for multiple objects detection in autonomous driving","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2896704645","doi":"https://doi.org/10.1109/ijcnn.2018.8489289","mag":"2896704645"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489289","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489289","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5053853035","display_name":"Yaran Chen","orcid":"https://orcid.org/0000-0001-9356-0610"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaran Chen","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100624298","display_name":"Dongbin Zhao","orcid":"https://orcid.org/0000-0001-8218-9633"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongbin Zhao","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327745","display_name":"Haoran Li","orcid":"https://orcid.org/0000-0003-2559-9585"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoran Li","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101459856","display_name":"Dong Li","orcid":"https://orcid.org/0000-0002-8766-500X"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Li","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020083340","display_name":"Ping Guo","orcid":"https://orcid.org/0000-0002-7122-1084"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Guo","raw_affiliation_strings":["School of Systems Science, Beijing Normal University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Systems Science, Beijing Normal University, China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9993000030517578,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991000294685364,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.8154679536819458},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8147662878036499},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7682110667228699},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.758740246295929},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5774811506271362},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5570785403251648},{"id":"https://openalex.org/keywords/viola\u2013jones-object-detection-framework","display_name":"Viola\u2013Jones object detection framework","score":0.510837972164154},{"id":"https://openalex.org/keywords/object-class-detection","display_name":"Object-class detection","score":0.5041617155075073},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.46100184321403503},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45302268862724304},{"id":"https://openalex.org/keywords/champion","display_name":"Champion","score":0.44689685106277466},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3501474857330322},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.27822980284690857},{"id":"https://openalex.org/keywords/face-detection","display_name":"Face detection","score":0.1434251368045807}],"concepts":[{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.8154679536819458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8147662878036499},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7682110667228699},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.758740246295929},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5774811506271362},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5570785403251648},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.510837972164154},{"id":"https://openalex.org/C71681937","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object-class detection","level":5,"score":0.5041617155075073},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.46100184321403503},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45302268862724304},{"id":"https://openalex.org/C2780465443","wikidata":"https://www.wikidata.org/wiki/Q852061","display_name":"Champion","level":2,"score":0.44689685106277466},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3501474857330322},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27822980284690857},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.1434251368045807},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489289","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489289","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2055022211","https://openalex.org/W2113926057","https://openalex.org/W2126452053","https://openalex.org/W2141461755","https://openalex.org/W2148958980","https://openalex.org/W2171243491","https://openalex.org/W2194775991","https://openalex.org/W2524008204","https://openalex.org/W2524771588","https://openalex.org/W2613718673","https://openalex.org/W2617116555","https://openalex.org/W2742332513","https://openalex.org/W2963150697","https://openalex.org/W3106250896","https://openalex.org/W6620707391","https://openalex.org/W6679160085","https://openalex.org/W6687483927","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W3204852000","https://openalex.org/W4205668735","https://openalex.org/W2901758161","https://openalex.org/W4297797959","https://openalex.org/W4297491714","https://openalex.org/W4387272257","https://openalex.org/W3114299475","https://openalex.org/W4312834249","https://openalex.org/W3108847140","https://openalex.org/W3209429418"],"abstract_inverted_index":{"This":[0,69],"paper":[1],"proposes":[2],"a":[3,31,98],"novel":[4],"vision-based":[5],"object":[6,25,28,79,110,146,157],"detection":[7,21,29,80,106,111,147,158,175],"method":[8,22,103,125],"in":[9,39,51,141,171,177],"autonomous":[10,35],"driving,":[11],"which":[12,76],"introduces":[13],"the":[14,18,40,57,89,109,115,123,129,132,136,153,164,169,172],"temporal":[15,149],"information":[16,150],"into":[17],"deep":[19],"learning-based":[20],"for":[23,34],"moving":[24,86],"detection.":[26],"Vision-based":[27],"is":[30,66,70],"critical":[32],"technology":[33],"driving.":[36],"The":[37,144],"objects":[38,61,134],"real":[41],"world":[42],"such":[43],"as":[44],"driving":[45],"cars,":[46],"don't":[47],"have":[48],"great":[49],"changes":[50],"their":[52],"positions":[53],"and":[54,100,121,139],"velocities.":[55],"So":[56],"position":[58],"change":[59],"of":[60,155],"between":[62,131],"two":[63],"consecutive":[64,92],"frames":[65,93],"not":[67],"large.":[68],"usually":[71,77],"ignored":[72],"by":[73],"traditional":[74],"works,":[75],"use":[78],"methods":[81],"on":[82,114,135,161],"still-images":[83],"to":[84,107,127],"detect":[85],"objects.":[87],"Considering":[88],"relationship":[90],"among":[91],"(temporal":[94],"information),":[95],"we":[96,167],"present":[97],"robust":[99],"real-time":[101],"tracking":[102,124],"following":[104],"image":[105],"refine":[108],"results.":[112],"Based":[113],"three":[116],"key":[117],"attributes":[118],"(distances,":[119],"sizes":[120],"positions),":[122],"aims":[126],"build":[128],"association":[130],"detected":[133],"current":[137],"frame":[138],"those":[140],"previous":[142],"frames.":[143],"proposed":[145,165],"with":[148],"dramatically":[151],"improves":[152],"performance":[154],"existing":[156],"algorithms":[159],"based":[160],"stillimage.":[162],"With":[163],"method,":[166],"won":[168],"champion":[170],"preceding":[173],"vehicle":[174,180],"task":[176],"2017":[178],"intelligent":[179],"future":[181],"challenge(2017":[182],"IVFC)":[183],"<sup":[184],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[185],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[186],".":[187]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}