{"id":"https://openalex.org/W3208316687","doi":"https://doi.org/10.1109/itsc48978.2021.9565049","title":"A New Online Approach for Moving Cast Shadow Suppression in Traffic Videos","display_name":"A New Online Approach for Moving Cast Shadow Suppression in Traffic Videos","publication_year":2021,"publication_date":"2021-09-19","ids":{"openalex":"https://openalex.org/W3208316687","doi":"https://doi.org/10.1109/itsc48978.2021.9565049","mag":"3208316687"},"language":"en","primary_location":{"id":"doi:10.1109/itsc48978.2021.9565049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc48978.2021.9565049","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/A5014533739","display_name":"Hadi Ghahremannezhad","orcid":"https://orcid.org/0000-0003-2795-4464"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hadi Ghahremannezhad","raw_affiliation_strings":["Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041683820","display_name":"Hang Shi","orcid":"https://orcid.org/0000-0002-6326-9942"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hang Shi","raw_affiliation_strings":["Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100454866","display_name":"Chengjun Liu","orcid":"https://orcid.org/0000-0002-2036-0770"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chengjun Liu","raw_affiliation_strings":["Faculty of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I118118575"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3034","last_page":"3039"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998000264167786,"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"}},"topics":[{"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9897000193595886,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T13890","display_name":"Remote Sensing and Land Use","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7910424470901489},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7527864575386047},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7097952961921692},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7037250399589539},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5797048211097717},{"id":"https://openalex.org/keywords/shadow","display_name":"Shadow (psychology)","score":0.5339212417602539},{"id":"https://openalex.org/keywords/brightness","display_name":"Brightness","score":0.5245556831359863},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5173968076705933},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49690893292427063},{"id":"https://openalex.org/keywords/shadow-mapping","display_name":"Shadow mapping","score":0.4841926395893097},{"id":"https://openalex.org/keywords/background-subtraction","display_name":"Background subtraction","score":0.47026023268699646},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4622739255428314},{"id":"https://openalex.org/keywords/chromaticity","display_name":"Chromaticity","score":0.461958646774292},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4426576793193817},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3108796775341034}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7910424470901489},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7527864575386047},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7097952961921692},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7037250399589539},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5797048211097717},{"id":"https://openalex.org/C117797892","wikidata":"https://www.wikidata.org/wiki/Q286363","display_name":"Shadow (psychology)","level":2,"score":0.5339212417602539},{"id":"https://openalex.org/C125245961","wikidata":"https://www.wikidata.org/wiki/Q221656","display_name":"Brightness","level":2,"score":0.5245556831359863},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5173968076705933},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49690893292427063},{"id":"https://openalex.org/C116544410","wikidata":"https://www.wikidata.org/wiki/Q1478122","display_name":"Shadow mapping","level":2,"score":0.4841926395893097},{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.47026023268699646},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4622739255428314},{"id":"https://openalex.org/C201780734","wikidata":"https://www.wikidata.org/wiki/Q5069880","display_name":"Chromaticity","level":2,"score":0.461958646774292},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4426576793193817},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3108796775341034},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc48978.2021.9565049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc48978.2021.9565049","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":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.6800000071525574}],"awards":[{"id":"https://openalex.org/G450488036","display_name":"US Ignite: Focus Area 1: Fast Autonomic Traffic Congestion Monitoring and Incident Detection through Advanced Networking, Edge Computing, and Video Analytics","funder_award_id":"1647170","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1967913888","https://openalex.org/W2008543119","https://openalex.org/W2100907567","https://openalex.org/W2118073342","https://openalex.org/W2123009513","https://openalex.org/W2128991274","https://openalex.org/W2143373691","https://openalex.org/W2144720632","https://openalex.org/W2303399114","https://openalex.org/W2521508828","https://openalex.org/W2808548587","https://openalex.org/W2817412859","https://openalex.org/W2895126795","https://openalex.org/W2903256124","https://openalex.org/W2931406161","https://openalex.org/W2989753370","https://openalex.org/W2995021820","https://openalex.org/W2999431534","https://openalex.org/W3006215266","https://openalex.org/W3007041910","https://openalex.org/W3014732394","https://openalex.org/W3027615165","https://openalex.org/W3097325428","https://openalex.org/W3129045723","https://openalex.org/W3129790038","https://openalex.org/W3171734802","https://openalex.org/W6755325445","https://openalex.org/W6790203541","https://openalex.org/W6790632315","https://openalex.org/W6796547613"],"related_works":["https://openalex.org/W2620469466","https://openalex.org/W2155354377","https://openalex.org/W2011323037","https://openalex.org/W1993524181","https://openalex.org/W4244119470","https://openalex.org/W2002381192","https://openalex.org/W4361022438","https://openalex.org/W2386749094","https://openalex.org/W2049028248","https://openalex.org/W39598702"],"abstract_inverted_index":{"In":[0],"applications":[1],"of":[2,77,95,142,147,154,184,199,228,234],"traffic":[3,219],"video":[4,212,220],"analysis,":[5],"moving":[6,55],"vehicles":[7],"can":[8],"induce":[9],"cast":[10,24,60],"shadows":[11,61,193],"that":[12],"have":[13],"negative":[14],"impacts":[15],"on":[16,139],"the":[17,42,54,63,74,81,87,113,116,145,152,160,166,189,197,200,224,232,235],"system":[18],"performance.":[19],"Here,":[20],"a":[21,182],"new":[22,100],"online":[23],"shadow":[25,68,102,137,207],"removal":[26],"method":[27,46,104,119],"is":[28,47,105,132],"proposed":[29,106,236],"which":[30,178],"integrates":[31],"pixel-based,":[32],"region-based,":[33],"and":[34,79,90,97,136,151,162,168,194,215,217],"statistical":[35],"modeling":[36],"techniques":[37],"to":[38,52,115,122,173,187],"detect":[39],"shadows.":[40],"Specifically,":[41],"global":[43],"foreground":[44,91,125,163,190],"modeling(GFM)":[45],"first":[48],"applied":[49],"in":[50,83,86,93,120,165],"order":[51,121],"segment":[53,131],"objects":[56],"along":[57],"with":[58],"their":[59],"from":[62],"stationary":[64],"background.":[65],"The":[66],"potential":[67,143],"pixels":[69,191],"are":[70,171,179,203],"identified":[71],"by":[72,181,223],"considering":[73],"physics-based":[75],"properties":[76],"reflection":[78],"comparing":[80],"changes":[82],"color":[84],"values":[85,164],"corresponding":[88],"background":[89,161],"locations":[92],"terms":[94],"brightness":[96],"chromaticity.":[98],"A":[99],"region-based":[101],"detection":[103],"using":[107,210],"an":[108],"illumination":[109],"invariant":[110],"feature":[111,176],"as":[112],"input":[114],"k-means":[117],"clustering":[118],"partition":[123],"each":[124],"component":[126],"into":[127,134,192],"separate":[128],"segments.":[129],"Each":[130],"classified":[133],"object":[135],"based":[138],"its":[140],"portion":[141],"shadows,":[144],"amount":[146],"gradient":[148],"information":[149],"introduced,":[150],"number":[153],"extrinsic":[155],"terminal":[156],"points":[157],"contained.":[158],"Afterward,":[159],"RGB":[167],"HSV":[169],"color-spaces":[170],"utilized":[172],"construct":[174],"six-dimensional":[175],"vectors":[177],"modeled":[180],"mixture":[183],"Gaussian":[185],"distributions":[186],"classify":[188],"objects.":[195],"Lastly,":[196],"results":[198],"previous":[201],"steps":[202],"integrated":[204],"for":[205],"final":[206],"detection.":[208],"Experiments":[209],"public":[211],"data":[213,221],"\u2018Highway-1\u2019":[214],"\u2018Highway-3\u2019,":[216],"real":[218],"provided":[222],"New":[225],"Jersey":[226],"Department":[227],"Transportation":[229],"(NJDOT)":[230],"demonstrate":[231],"effectiveness":[233],"method.":[237]},"counts_by_year":[{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":7}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
