{"id":"https://openalex.org/W3210941561","doi":"https://doi.org/10.1109/itsc48978.2021.9564655","title":"A Graph Convolutional Neural Network Based Approach for Traffic Monitoring Using Augmented Detections with Optical Flow","display_name":"A Graph Convolutional Neural Network Based Approach for Traffic Monitoring Using Augmented Detections with Optical Flow","publication_year":2021,"publication_date":"2021-09-19","ids":{"openalex":"https://openalex.org/W3210941561","doi":"https://doi.org/10.1109/itsc48978.2021.9564655","mag":"3210941561"},"language":"en","primary_location":{"id":"doi:10.1109/itsc48978.2021.9564655","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc48978.2021.9564655","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Intelligent Transportation Systems Conference (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/A5065209719","display_name":"Ioannis Papakis","orcid":null},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ioannis Papakis","raw_affiliation_strings":["Department of Computer Science of Virginia Tech,Blacksburg,VA,24061"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science of Virginia Tech,Blacksburg,VA,24061","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080208733","display_name":"Abhijit Sarkar","orcid":"https://orcid.org/0000-0003-0525-5240"},"institutions":[{"id":"https://openalex.org/I4387930269","display_name":"Virginia Tech Transportation Institute","ror":"https://ror.org/05953j253","country_code":null,"type":"facility","lineage":["https://openalex.org/I4387930269","https://openalex.org/I859038795"]},{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhijit Sarkar","raw_affiliation_strings":["Virginia Tech Transportation Institute, Blacksburg, VA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Virginia Tech Transportation Institute, Blacksburg, VA","institution_ids":["https://openalex.org/I4387930269","https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081622450","display_name":"Anuj Karpatne","orcid":"https://orcid.org/0000-0003-1647-3534"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anuj Karpatne","raw_affiliation_strings":["Department of Computer Science of Virginia Tech,Blacksburg,VA,24061"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science of Virginia Tech,Blacksburg,VA,24061","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7638,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.81405302,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2980","last_page":"2986"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9977999925613403,"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/computer-science","display_name":"Computer science","score":0.7896941900253296},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6509703397750854},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6240192651748657},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.5898873209953308},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5767207741737366},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.5408738851547241},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4928036034107208},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.48681876063346863},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4784986078739166},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.447068452835083},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4267004132270813},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.42302757501602173},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4131201207637787},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41137874126434326},{"id":"https://openalex.org/keywords/scene-graph","display_name":"Scene graph","score":0.41078007221221924},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3657130300998688},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.34666311740875244},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.14426299929618835}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7896941900253296},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6509703397750854},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6240192651748657},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.5898873209953308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5767207741737366},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.5408738851547241},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4928036034107208},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.48681876063346863},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4784986078739166},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.447068452835083},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4267004132270813},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.42302757501602173},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4131201207637787},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41137874126434326},{"id":"https://openalex.org/C179372163","wikidata":"https://www.wikidata.org/wiki/Q1406181","display_name":"Scene graph","level":3,"score":0.41078007221221924},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3657130300998688},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.34666311740875244},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.14426299929618835},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"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/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc48978.2021.9564655","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc48978.2021.9564655","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Intelligent Transportation Systems Conference (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1563795667","https://openalex.org/W1861492603","https://openalex.org/W1978491093","https://openalex.org/W2155680787","https://openalex.org/W2168356304","https://openalex.org/W2222512263","https://openalex.org/W2291627510","https://openalex.org/W2474389331","https://openalex.org/W2525653047","https://openalex.org/W2560474170","https://openalex.org/W2603203130","https://openalex.org/W2613718673","https://openalex.org/W2765877758","https://openalex.org/W2769415237","https://openalex.org/W2779627226","https://openalex.org/W2784549149","https://openalex.org/W2884732896","https://openalex.org/W2889237755","https://openalex.org/W2895150009","https://openalex.org/W2900201163","https://openalex.org/W2900327972","https://openalex.org/W2906499972","https://openalex.org/W2910057067","https://openalex.org/W2913841731","https://openalex.org/W2918125535","https://openalex.org/W2918745114","https://openalex.org/W2920942303","https://openalex.org/W2950832586","https://openalex.org/W2953759675","https://openalex.org/W2955970367","https://openalex.org/W2961663897","https://openalex.org/W2963037989","https://openalex.org/W2963063317","https://openalex.org/W2963446712","https://openalex.org/W2964015640","https://openalex.org/W2964241181","https://openalex.org/W2965843888","https://openalex.org/W2979917379","https://openalex.org/W2981393651","https://openalex.org/W2983208726","https://openalex.org/W2986732333","https://openalex.org/W2991384465","https://openalex.org/W2997866947","https://openalex.org/W3005195333","https://openalex.org/W3009138919","https://openalex.org/W3010309142","https://openalex.org/W3011353537","https://openalex.org/W3034240185","https://openalex.org/W3034275286","https://openalex.org/W3034679090","https://openalex.org/W3034739212","https://openalex.org/W3035442500","https://openalex.org/W3039256091","https://openalex.org/W3099887740","https://openalex.org/W3106250896","https://openalex.org/W4206471589","https://openalex.org/W4289753942","https://openalex.org/W6628973269","https://openalex.org/W6639102338","https://openalex.org/W6725739302","https://openalex.org/W6765860775","https://openalex.org/W6779950659","https://openalex.org/W6785652829","https://openalex.org/W6785742526"],"related_works":["https://openalex.org/W2343908003","https://openalex.org/W2922421953","https://openalex.org/W3163104128","https://openalex.org/W3177406559","https://openalex.org/W4288094939","https://openalex.org/W2534746541","https://openalex.org/W2059299633","https://openalex.org/W2381610189","https://openalex.org/W2079759540","https://openalex.org/W2148005459"],"abstract_inverted_index":{"This":[0,51],"paper":[1],"presents":[2],"a":[3,128],"novel":[4],"method":[5],"for":[6,22,94],"Multi-Object":[7],"Tracking":[8],"(MOT)":[9],"using":[10,143],"Graph":[11,26],"Convolutional":[12],"Neural":[13],"Network":[14],"based":[15,27],"feature":[16,20,49],"extraction":[17],"and":[18,32,67,193],"end-to-end":[19,95],"matching":[21],"object":[23,111,151],"association.":[24],"The":[25,105],"approach":[28,130],"incorporates":[29],"both":[30],"appearance":[31],"geometry":[33,64],"of":[34,48,62,65,77,83,90,97,185],"objects":[35,66,101],"at":[36],"past":[37],"frames":[38],"as":[39,41,149,157],"well":[40],"the":[42,46,55,59,63,72,75,84,88,91,98,120,125,183,186,190,194],"current":[43,173],"frame":[44,137,142],"into":[45,115,139],"task":[47],"learning.":[50],"new":[52,129,141,160],"paradigm":[53],"enables":[54],"network":[56,106],"to":[57,70,109,119],"leverage":[58],"\u201ccontext\u201d":[60],"information":[61],"allows":[68],"us":[69],"model":[71,103],"interactions":[73],"among":[74,100],"features":[76],"multiple":[78],"objects.":[79,158],"Another":[80],"central":[81],"innovation":[82],"proposed":[85,187],"framework":[86],"is":[87,107,131,164],"use":[89],"Sinkhorn":[92],"algorithm":[93],"learning":[96],"associations":[99,112],"during":[102],"training.":[104],"trained":[108],"predict":[110],"by":[113],"taking":[114],"account":[116],"constraints":[117],"specific":[118],"MOT":[121,196],"task.":[122],"To":[123],"increase":[124],"detector's":[126],"sensitivity,":[127],"also":[132],"presented":[133],"that":[134],"propagates":[135],"previous":[136],"detections":[138],"each":[140],"optical":[144],"flow.":[145],"These":[146],"are":[147,154],"treated":[148],"added":[150],"proposals":[152],"which":[153,167],"then":[155],"classified":[156],"A":[159],"traffic":[161],"monitoring":[162],"dataset":[163,192],"additionally":[165],"provided,":[166],"includes":[168],"naturalistic":[169],"video":[170],"footage":[171],"from":[172],"infrastructure":[174],"cameras":[175],"in":[176],"Virginia":[177],"Beach":[178],"City.":[179],"Experimental":[180],"evaluation":[181],"demonstrates":[182],"efficacy":[184],"approaches":[188],"on":[189],"provided":[191],"popular":[195],"Challenge":[197],"Benchmark.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
