{"id":"https://openalex.org/W2081532106","doi":"https://doi.org/10.1109/itsc.2013.6728472","title":"Flexible solution for modeling and tracking generic dynamic 3D environments","display_name":"Flexible solution for modeling and tracking generic dynamic 3D environments","publication_year":2013,"publication_date":"2013-10-01","ids":{"openalex":"https://openalex.org/W2081532106","doi":"https://doi.org/10.1109/itsc.2013.6728472","mag":"2081532106"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2013.6728472","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2013.6728472","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)","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/A5014747694","display_name":"Radu D\u0103nescu","orcid":"https://orcid.org/0000-0002-4515-8114"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Radu Danescu","raw_affiliation_strings":["Technical University of Cluj-Napoca, Cluj-Napoca, Romania","Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca, Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]},{"raw_affiliation_string":"Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047954457","display_name":"Sergiu Nedevschi","orcid":"https://orcid.org/0000-0003-2018-4647"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Sergiu Nedevschi","raw_affiliation_strings":["S. Nedevschi is with the Technical University of Cluj-Napoca, Cluj-Napoca, Romania","Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"S. Nedevschi is with the Technical University of Cluj-Napoca, Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]},{"raw_affiliation_string":"Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I158333966"],"apc_list":null,"apc_paid":null,"fwci":0.6236,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.70185185,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1686","last_page":"1692"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991000294685364,"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"}},"topics":[{"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"}},{"id":"https://openalex.org/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.9883999824523926,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10531","display_name":"Advanced Vision and Imaging","score":0.9865000247955322,"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":0.72980797290802},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7230568528175354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7183528542518616},{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.6670059561729431},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6360341310501099},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.5758011341094971},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.5363738536834717},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.49310705065727234},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4926324486732483},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.4712691009044647},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4168202877044678},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.1731051802635193}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.72980797290802},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7230568528175354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7183528542518616},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.6670059561729431},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6360341310501099},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5758011341094971},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5363738536834717},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.49310705065727234},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4926324486732483},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.4712691009044647},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4168202877044678},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.1731051802635193},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"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/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2013.6728472","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2013.6728472","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1508161315","https://openalex.org/W1589504938","https://openalex.org/W1976511700","https://openalex.org/W1982739567","https://openalex.org/W1999050017","https://openalex.org/W2016665050","https://openalex.org/W2025340773","https://openalex.org/W2034430035","https://openalex.org/W2037490150","https://openalex.org/W2107516554","https://openalex.org/W2112444548","https://openalex.org/W2116764847","https://openalex.org/W2119404509","https://openalex.org/W2130545744","https://openalex.org/W2143220699","https://openalex.org/W2143324623","https://openalex.org/W2145951052","https://openalex.org/W2161406034","https://openalex.org/W2173899871","https://openalex.org/W2776260803","https://openalex.org/W3022352042"],"related_works":["https://openalex.org/W2927378857","https://openalex.org/W2801696468","https://openalex.org/W2406829934","https://openalex.org/W1824810860","https://openalex.org/W2185006999","https://openalex.org/W2162253570","https://openalex.org/W1583020711","https://openalex.org/W2138381686","https://openalex.org/W2010934810","https://openalex.org/W3144709167"],"abstract_inverted_index":{"The":[0,73,108,143,162,199],"traffic":[1,37],"environment":[2,34,190],"is":[3,63,90,111,150,168,202],"a":[4,26,78,93,114,172],"dynamic":[5,32,42,163,197],"and":[6,17,87,135,174,185,192],"complex":[7],"3D":[8,33,210],"scene,":[9,38,180],"which":[10,97,126],"needs":[11],"accurate":[12,175,204],"models":[13],"to":[14,20,103,170,181,193],"represent":[15],"it":[16,195],"reliable":[18],"algorithms":[19],"perceive":[21],"it.":[22],"This":[23],"paper":[24],"presents":[25],"new":[27,51],"model":[28,145],"for":[29,65,147],"representing":[30],"the":[31,36,39,56,67,70,83,119,123,129,136,139,156,159,178,183,188,208],"of":[35,69,77,95,128,138,158,177,187,207],"particle":[40,109,148],"based":[41,54,117],"elevation":[43,164],"map":[44,79,165],"enhanced":[45],"with":[46,122,196],"gray":[47,88],"level":[48],"information.":[49,198],"A":[50],"tracking":[52,166],"algorithm,":[53],"on":[55,118],"measurement":[57,124,144],"cues":[58],"extracted":[59],"from":[60,100,133],"dense":[61,173],"stereovision,":[62],"employed":[64],"estimating":[66],"state":[68,81],"proposed":[71],"model.":[72],"multimodal":[74],"probability":[75],"density":[76,184],"cell's":[80,84],"concerning":[82],"speed,":[85],"height":[86],"level,":[89],"approximated":[91],"by":[92,152],"population":[94,110],"particles,":[96],"can":[98],"migrate":[99],"one":[101],"cell":[102],"another":[104],"using":[105,113],"their":[106],"speeds.":[107],"updated":[112],"weighting-resampling":[115],"mechanism":[116],"particle's":[120],"fitness":[121],"data,":[125],"consists":[127],"raw":[130],"heights":[131],"obtained":[132],"stereovision":[134,160],"pixels":[137],"grayscale":[140],"left":[141],"image.":[142],"used":[146],"weighting":[149],"designed":[151],"taking":[153],"into":[154],"account":[155],"specifics":[157],"sensor.":[161],"system":[167],"able":[169],"provide":[171],"representation":[176,206],"observed":[179],"improve":[182],"accuracy":[186],"stereo-based":[189],"perception,":[191],"enhance":[194],"final":[200],"result":[201],"an":[203],"virtual":[205],"perceived":[209],"scene.":[211]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}