{"id":"https://openalex.org/W2793939440","doi":"https://doi.org/10.1109/itsc.2017.8317600","title":"Convolutional gated recurrent networks for video semantic segmentation in automated driving","display_name":"Convolutional gated recurrent networks for video semantic segmentation in automated driving","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2793939440","doi":"https://doi.org/10.1109/itsc.2017.8317600","mag":"2793939440"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2017.8317600","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2017.8317600","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)","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/A5022335103","display_name":"Mennatullah Siam","orcid":"https://orcid.org/0000-0003-1854-3698"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mennatullah Siam","raw_affiliation_strings":["University of Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091664745","display_name":"Sepehr Valipour","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Sepehr Valipour","raw_affiliation_strings":["University of Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030948380","display_name":"Martin J\u00e4gersand","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Martin Jagersand","raw_affiliation_strings":["University of Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082800075","display_name":"Nilanjan Ray","orcid":"https://orcid.org/0000-0002-7588-5400"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Nilanjan Ray","raw_affiliation_strings":["University of Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014764449","display_name":"Senthil Yogamani","orcid":"https://orcid.org/0000-0003-3755-4245"},"institutions":[{"id":"https://openalex.org/I4210126639","display_name":"Valeo (Ireland)","ror":"https://ror.org/031sgpn76","country_code":"IE","type":"company","lineage":["https://openalex.org/I220619192","https://openalex.org/I4210126639"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Senthil Yogamani","raw_affiliation_strings":["Valeo Vision Systems, Ireland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Valeo Vision Systems, Ireland","institution_ids":["https://openalex.org/I4210126639"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2388,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.6459698,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/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/T11605","display_name":"Visual Attention and Saliency Detection","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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8665359020233154},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7937413454055786},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6883671283721924},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5696462392807007},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4657514691352844},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4226524233818054},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4178842604160309},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.416519433259964},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4125255346298218}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8665359020233154},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7937413454055786},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6883671283721924},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5696462392807007},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4657514691352844},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4226524233818054},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4178842604160309},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.416519433259964},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4125255346298218}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2017.8317600","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2017.8317600","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)","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":38,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W1542723449","https://openalex.org/W1606347560","https://openalex.org/W1679528986","https://openalex.org/W1686810756","https://openalex.org/W1745334888","https://openalex.org/W1903029394","https://openalex.org/W1910657905","https://openalex.org/W1913356549","https://openalex.org/W1924770834","https://openalex.org/W1947481528","https://openalex.org/W1961270558","https://openalex.org/W2009874829","https://openalex.org/W2011953904","https://openalex.org/W2064675550","https://openalex.org/W2107878631","https://openalex.org/W2109521789","https://openalex.org/W2115730999","https://openalex.org/W2124592697","https://openalex.org/W2138682569","https://openalex.org/W2167687475","https://openalex.org/W2171295735","https://openalex.org/W2178257216","https://openalex.org/W2210040239","https://openalex.org/W2212077366","https://openalex.org/W2340897893","https://openalex.org/W2431874326","https://openalex.org/W2463175074","https://openalex.org/W2470139095","https://openalex.org/W2560474170","https://openalex.org/W2561523096","https://openalex.org/W2953296820","https://openalex.org/W2963149042","https://openalex.org/W2963631529","https://openalex.org/W2963758027","https://openalex.org/W2963881378","https://openalex.org/W2964199361","https://openalex.org/W6636358008"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W4293226380","https://openalex.org/W2183306018","https://openalex.org/W2549990292","https://openalex.org/W2345479200","https://openalex.org/W2951819827","https://openalex.org/W2849310602","https://openalex.org/W4321487865","https://openalex.org/W2419146053","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,21,43,66,155,172,199],"is":[2,53,84,139],"an":[3,23,113],"important":[4],"visual":[5],"perception":[6],"module":[7],"of":[8,12,77,95,100],"automated":[9,201],"driving.":[10],"Most":[11],"the":[13,37,41,103,131,168],"progress":[14],"has":[15,191],"been":[16,192],"focused":[17],"on":[18,148],"single-frame":[19],"image":[20],"with":[22,116],"optional":[24],"temporal":[25,38],"post-processing.":[26],"In":[27],"this":[28,213],"paper,":[29],"we":[30,204],"propose":[31],"a":[32,56,78,87],"novel":[33],"algorithm":[34,74],"to":[35,54,107],"utilize":[36],"information":[39],"in":[40,134,143,162,173,180,212],"semantic":[42,171],"model":[44,115],"using":[45,156],"convolutional":[46,80],"gated":[47,88],"recurrent":[48,89],"networks.":[49],"The":[50,72,150],"main":[51],"motivation":[52],"design":[55],"spatio-temporal":[57,197],"network":[58,81],"which":[59],"can":[60,105],"leverage":[61],"motion":[62],"cues":[63],"for":[64,121,153,170,194,200],"aiding":[65],"and":[67,98,102,137,160,164,167,178,182,203],"providing":[68],"temporally":[69],"consistent":[70,127],"results.":[71],"proposed":[73],"makes":[75],"use":[76,92],"fully":[79],"(FCN)":[82],"that":[83,206],"embedded":[85],"into":[86],"architecture.":[90],"We":[91,110],"FCN":[93,114,133],"because":[94],"its":[96],"simplicity":[97],"ease":[99],"extension":[101],"embedding":[104],"extend":[106],"other":[108],"architectures.":[109],"also":[111,140],"chose":[112],"reasonable":[117],"computational":[118],"complexity":[119],"suitable":[120],"real-time":[122],"applications.":[123],"Experimental":[124],"results":[125,208],"show":[126],"accuracy":[128,151],"improvements":[129,152,169],"over":[130],"baseline":[132],"several":[135],"datasets":[136],"it":[138],"visually":[141],"evident":[142],"our":[144,186,207],"test":[145],"videos":[146],"shared":[147],"YouTube.":[149],"binary":[154],"F-measure":[157],"were":[158,176],"5%":[159],"3%":[161],"SegTrack2":[163],"Davis":[165],"respectively":[166],"mean":[174],"IoU":[175],"5.7%":[177],"1.7%":[179],"Synthia":[181],"Camvid":[183],"respectively.":[184],"To":[185],"knowledge,":[187],"no":[188],"prior":[189],"work":[190],"done":[193],"CNN":[195],"based":[196],"video":[198],"driving":[202],"hope":[205],"encourage":[209],"further":[210],"research":[211],"area.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
