{"id":"https://openalex.org/W1899675034","doi":"https://doi.org/10.1109/iccv.2001.937590","title":"A co-inference approach to robust visual tracking","display_name":"A co-inference approach to robust visual tracking","publication_year":2002,"publication_date":"2002-11-13","ids":{"openalex":"https://openalex.org/W1899675034","doi":"https://doi.org/10.1109/iccv.2001.937590","mag":"1899675034"},"language":"en","primary_location":{"id":"doi:10.1109/iccv.2001.937590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv.2001.937590","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","raw_type":"proceedings-article"},"type":"article","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/A5101780958","display_name":"Ying Wu","orcid":"https://orcid.org/0009-0001-6768-5118"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ying Wu","raw_affiliation_strings":["Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA","Beckman Inst. for Adv. Sci. and Technol., Illinois Univ., Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA","institution_ids":[]},{"raw_affiliation_string":"Beckman Inst. for Adv. Sci. and Technol., Illinois Univ., Urbana, IL, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101457342","display_name":"Thomas S. Huang","orcid":"https://orcid.org/0000-0001-8474-5859"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"T.S. Huang","raw_affiliation_strings":["[Beckman Institute, University of Illinois, Urbana-Champaign, Urbana, IL, USA]"],"affiliations":[{"raw_affiliation_string":"[Beckman Institute, University of Illinois, Urbana-Champaign, Urbana, IL, USA]","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101780958"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":38.0375,"has_fulltext":false,"cited_by_count":127,"citation_normalized_percentile":{"value":0.99846876,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"2","issue":null,"first_page":"26","last_page":"33"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.9918000102043152,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7196642160415649},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6523484587669373},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6359195709228516},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.603668749332428},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.5435779094696045},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.537568211555481},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.4886482357978821},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.46319857239723206},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.448154091835022},{"id":"https://openalex.org/keywords/approximate-inference","display_name":"Approximate inference","score":0.4276205599308014},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.42676109075546265},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3592875599861145},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3554726541042328}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7196642160415649},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6523484587669373},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6359195709228516},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.603668749332428},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.5435779094696045},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.537568211555481},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.4886482357978821},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.46319857239723206},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.448154091835022},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.4276205599308014},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.42676109075546265},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3592875599861145},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3554726541042328},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iccv.2001.937590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv.2001.937590","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.160.7507","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.7507","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ece.northwestern.edu/~yingwu/papers/conference/2001/iccv01a_coinference_final.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"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":29,"referenced_works":["https://openalex.org/W1481420047","https://openalex.org/W1512570974","https://openalex.org/W1516111018","https://openalex.org/W1528056001","https://openalex.org/W1533614607","https://openalex.org/W1910898529","https://openalex.org/W2049633694","https://openalex.org/W2064480843","https://openalex.org/W2103243027","https://openalex.org/W2118836531","https://openalex.org/W2124068852","https://openalex.org/W2126736494","https://openalex.org/W2140235142","https://openalex.org/W2142226088","https://openalex.org/W2143073828","https://openalex.org/W2144124341","https://openalex.org/W2145443310","https://openalex.org/W2152057642","https://openalex.org/W2159128898","https://openalex.org/W2168682262","https://openalex.org/W2914885528","https://openalex.org/W3160881378","https://openalex.org/W4255333169","https://openalex.org/W6628883542","https://openalex.org/W6632023624","https://openalex.org/W6640096618","https://openalex.org/W6681106479","https://openalex.org/W6682180107","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W2776613281","https://openalex.org/W4226287370","https://openalex.org/W2962950510","https://openalex.org/W2114556850","https://openalex.org/W2153267847","https://openalex.org/W3021913283","https://openalex.org/W2159992248","https://openalex.org/W959529772","https://openalex.org/W4297812452","https://openalex.org/W2070797946"],"abstract_inverted_index":{"Visual":[0],"tracking":[1,29,45,56,123],"could":[2],"be":[3],"treated":[4],"as":[5],"a":[6,46,64,85,131,149],"parameter":[7],"estimation":[8,48],"problem":[9,54],"of":[10,27,38,55,76,103,130,152],"target":[11,21,132],"representation":[12],"based":[13,106],"on":[14,107],"observations":[15],"in":[16,30,63,116,135,148,158],"image":[17],"sequences.":[18],"A":[19,91],"richer":[20],"representations":[22],"would":[23],"incur":[24],"better":[25],"chances":[26],"successful":[28],"cluttered":[31],"and":[32,57,67,83,127],"dynamic":[33],"environments.":[34],"However,":[35],"the":[36,53,104,108,143,162],"dimensionality":[37],"target's":[39],"state":[40],"space":[41],"also":[42],"increases":[43],"making":[44],"formidable":[47],"problem.":[49],"In":[50],"this":[51,136,159],"paper,":[52],"integrating":[58],"multiple":[59],"cues":[60],"is":[61,96,114,133],"formulated":[62],"probabilistic":[65],"framework;":[66],"represented":[68],"by":[69],"factorized":[70],"graphical":[71,78],"model.":[72],"Structured":[73],"variational":[74],"analysis":[75],"such":[77],"model":[79],"factorizes":[80],"different":[81],"modalities":[82],"suggests":[84],"co-inference":[86,105],"process":[87],"among":[88],"these":[89],"modalities.":[90],"sequential":[92],"Monte":[93],"Carlo":[94],"algorithm":[95,113,145],"proposed":[97,144],"to":[98,164],"give":[99],"an":[100],"efficient":[101],"approximation":[102],"importance":[109],"sampling":[110],"technique.":[111],"This":[112],"implemented":[115],"real-time":[117],"at":[118],"around":[119],"30":[120],"Hz.":[121],"Specifically,":[122],"both":[124],"position,":[125],"shape":[126],"color":[128],"distribution":[129],"investigated":[134],"paper.":[137],"Our":[138],"extensive":[139],"experiments":[140],"show":[141],"that":[142],"performs":[146],"robustly":[147],"large":[150],"variety":[151],"trucking":[153],"scenarios.":[154],"The":[155],"approach":[156],"presented":[157],"paper":[160],"has":[161],"potential":[163],"solve":[165],"other":[166],"sensor":[167],"fusion":[168],"problems.":[169]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":4}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}