{"id":"https://openalex.org/W2073863917","doi":"https://doi.org/10.1145/1026799.1026817","title":"Adaptive object tracking using bayesian network and memory","display_name":"Adaptive object tracking using bayesian network and memory","publication_year":2004,"publication_date":"2004-10-15","ids":{"openalex":"https://openalex.org/W2073863917","doi":"https://doi.org/10.1145/1026799.1026817","mag":"2073863917"},"language":"en","primary_location":{"id":"doi:10.1145/1026799.1026817","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1026799.1026817","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks","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/A5062145927","display_name":"Hang-Bong Kang","orcid":"https://orcid.org/0000-0002-7064-478X"},"institutions":[{"id":"https://openalex.org/I87111246","display_name":"Catholic University of Korea","ror":"https://ror.org/01fpnj063","country_code":"KR","type":"education","lineage":["https://openalex.org/I87111246"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hang-Bong Kang","raw_affiliation_strings":["Catholic University of Korea, Puchon city, Kyonggi-Do, Korea"],"affiliations":[{"raw_affiliation_string":"Catholic University of Korea, Puchon city, Kyonggi-Do, Korea","institution_ids":["https://openalex.org/I87111246"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101876695","display_name":"Sang Hyun Cho","orcid":"https://orcid.org/0000-0001-8289-1190"},"institutions":[{"id":"https://openalex.org/I87111246","display_name":"Catholic University of Korea","ror":"https://ror.org/01fpnj063","country_code":"KR","type":"education","lineage":["https://openalex.org/I87111246"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sang-Hyun Cho","raw_affiliation_strings":["Catholic University of Korea, Puchon city, Kyonggi-Do, Korea"],"affiliations":[{"raw_affiliation_string":"Catholic University of Korea, Puchon city, Kyonggi-Do, Korea","institution_ids":["https://openalex.org/I87111246"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5062145927"],"corresponding_institution_ids":["https://openalex.org/I87111246"],"apc_list":null,"apc_paid":null,"fwci":0.8199,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.75033097,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"104","last_page":"113"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9764999747276306,"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/T12496","display_name":"Color perception and design","score":0.935699999332428,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7664425373077393},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7057719826698303},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.6659857034683228},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.6401801109313965},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6118133068084717},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5313571095466614},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5188292264938354},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5031432509422302},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4977872371673584},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4959810674190521},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.48941031098365784},{"id":"https://openalex.org/keywords/dynamic-bayesian-network","display_name":"Dynamic Bayesian network","score":0.47904813289642334},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.422042578458786}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7664425373077393},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7057719826698303},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.6659857034683228},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.6401801109313965},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6118133068084717},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5313571095466614},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5188292264938354},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5031432509422302},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4977872371673584},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4959810674190521},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.48941031098365784},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.47904813289642334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.422042578458786},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1026799.1026817","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1026799.1026817","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4000000059604645,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1495303948","https://openalex.org/W2072905107","https://openalex.org/W2105934661","https://openalex.org/W2131393045","https://openalex.org/W2132103241","https://openalex.org/W2147370063","https://openalex.org/W2159128898","https://openalex.org/W2161406034","https://openalex.org/W2613779721","https://openalex.org/W3160881378"],"related_works":["https://openalex.org/W1964038743","https://openalex.org/W3128072696","https://openalex.org/W2578973671","https://openalex.org/W2215058820","https://openalex.org/W2097663773","https://openalex.org/W1602184117","https://openalex.org/W2413421635","https://openalex.org/W2511198839","https://openalex.org/W1966557338","https://openalex.org/W2366931106"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"an":[3],"adaptive":[4],"object":[5,95],"tracking":[6,121],"method":[7,35,110],"that":[8,19],"integrates":[9],"the":[10,43,66,75,80],"cues":[11],"from":[12],"color":[13,37],"likelihood":[14],"and":[15,18,38,51,113],"edge":[16,39],"likelihood,":[17],"adapts":[20],"itself":[21],"to":[22,78,92,104],"abrupt":[23,106],"appearance":[24,47,100,107],"changing":[25],"objects.":[26],"We":[27],"use":[28],"a":[29,56,98,116],"Bayesian":[30,76],"network":[31,77],"based":[32],"multi-modal":[33],"fusion":[34],"of":[36,45,53,82],"information.":[40],"To":[41],"handle":[42,105],"cases":[44],"sudden":[46],"changes,":[48],"occlusion,":[49],"disappearance":[50],"reappearance":[52],"tracked":[54],"objects,":[55],"memory":[57],"model":[58,101],"is":[59,102,111],"also":[60],"introduced.":[61],"The":[62],"proposed":[63],"tracker":[64],"has":[65],"following":[67],"characteristics.":[68],"First,":[69],"multiple":[70],"modalities":[71],"are":[72,88,123],"integrated":[73],"in":[74,90],"evaluate":[79],"posterior":[81],"each":[83],"feature.":[84],"Secondly,":[85],"context":[86],"factors":[87],"computed":[89],"order":[91],"select":[93],"best":[94],"state.":[96],"Finally,":[97],"memory-based":[99],"introduced":[103],"changes.":[108],"Our":[109],"robust":[112],"versatile":[114],"for":[115],"modest":[117],"computational":[118],"cost.":[119],"Desirable":[120],"results":[122],"obtained.":[124]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}