{"id":"https://openalex.org/W2147609308","doi":"https://doi.org/10.1109/wacv.2008.4544036","title":"Likelihood Map Fusion for Visual Object Tracking","display_name":"Likelihood Map Fusion for Visual Object Tracking","publication_year":2008,"publication_date":"2008-01-01","ids":{"openalex":"https://openalex.org/W2147609308","doi":"https://doi.org/10.1109/wacv.2008.4544036","mag":"2147609308"},"language":"en","primary_location":{"id":"doi:10.1109/wacv.2008.4544036","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2008.4544036","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Workshop on Applications of Computer Vision","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/A5080054465","display_name":"Zhaozheng Yin","orcid":"https://orcid.org/0000-0002-9602-6488"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhaozheng Yin","raw_affiliation_strings":["Pennsylvania State University, University Park, PA, USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038550641","display_name":"Fatih Porikli","orcid":"https://orcid.org/0000-0002-1520-4466"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fatih Porikli","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081509021","display_name":"Robert T. Collins","orcid":"https://orcid.org/0000-0001-9062-4252"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert T. Collins","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5080054465"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":7.0991,"has_fulltext":false,"cited_by_count":60,"citation_normalized_percentile":{"value":0.97330274,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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":1.0,"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":0.9988999962806702,"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"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9948999881744385,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.829731822013855},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7436937093734741},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6921201348304749},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.642837405204773},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6286378502845764},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5952407121658325},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.533418595790863},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.526155412197113},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.5070676803588867},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4935873746871948},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4830680191516876},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.4631355106830597},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4339215159416199},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.416128009557724},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19614005088806152},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07656455039978027}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.829731822013855},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7436937093734741},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6921201348304749},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.642837405204773},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6286378502845764},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5952407121658325},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.533418595790863},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.526155412197113},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.5070676803588867},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4935873746871948},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4830680191516876},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.4631355106830597},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4339215159416199},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.416128009557724},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19614005088806152},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07656455039978027},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/wacv.2008.4544036","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2008.4544036","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Workshop on Applications of Computer Vision","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.417.5701","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.417.5701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.merl.com/publications/docs/TR2008-008.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.579.2847","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.579.2847","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cse.psu.edu/~rcollins/Papers/fusionWACV2008.pdf","raw_type":"text"},{"id":"pmh:oai:openresearch-repository.anu.edu.au:1885/33644","is_oa":false,"landing_page_url":"http://hdl.handle.net/1885/33644","pdf_url":null,"source":{"id":"https://openalex.org/S4306402539","display_name":"ANU Open Research (Australian National University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I118347636","host_organization_name":"Australian National University","host_organization_lineage":["https://openalex.org/I118347636"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of IEEE Workshop on Applications of Computer Vision (WACV 2008)","raw_type":"Conference paper"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1534477342","https://openalex.org/W2113050750","https://openalex.org/W2128272608","https://openalex.org/W2132103241","https://openalex.org/W2144317099","https://openalex.org/W2146103513","https://openalex.org/W2149077040","https://openalex.org/W2150000644","https://openalex.org/W2154496665","https://openalex.org/W2158275940","https://openalex.org/W2161969291","https://openalex.org/W2293873019","https://openalex.org/W2970877003","https://openalex.org/W4254582343","https://openalex.org/W6676832264"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W1482209366","https://openalex.org/W2110523656","https://openalex.org/W2012200063","https://openalex.org/W2965594636"],"abstract_inverted_index":{"Visual":[0],"object":[1,37],"tracking":[2],"can":[3,50],"be":[4,52],"considered":[5],"as":[6],"a":[7,20,60,72],"figure-ground":[8],"classification":[9,92],"task.":[10],"In":[11],"this":[12],"paper,":[13],"different":[14],"features":[15,70],"are":[16],"used":[17,53],"to":[18,35,54,66,86,131,143],"generate":[19,55],"set":[21],"of":[22,31],"likelihood":[23,56,62,83,114],"maps":[24,84],"for":[25,77],"each":[26],"pixel":[27,33],"indicating":[28],"the":[29,81,87,98,107,112],"probability":[30],"that":[32],"belonging":[34],"foreground":[36],"or":[38],"scene":[39],"background.":[40],"For":[41],"example,":[42],"intensity,":[43],"texture,":[44],"motion,":[45],"saliency":[46],"and":[47,139],"template":[48],"matching":[49],"all":[51],"maps.":[57],"We":[58],"propose":[59],"generic":[61],"map":[63],"fusion":[64],"framework":[65,104,127],"combine":[67],"these":[68],"heterogeneous":[69],"into":[71],"fused":[73,113],"soft":[74],"segmentation":[75,88],"suitable":[76],"mean-shift":[78],"tracking.":[79],"All":[80],"component":[82],"contribute":[85],"based":[89],"on":[90],"their":[91],"confidence":[93],"scores":[94],"(weights)":[95],"learned":[96],"from":[97,135],"previous":[99],"frame.":[100],"The":[101,126],"evidence":[102],"combination":[103],"dynamically":[105],"updates":[106],"weights":[108],"such":[109],"that,":[110],"in":[111],"map,":[115],"discriminative":[116],"foreground/background":[117],"information":[118,123],"is":[119,124,128,140],"preserved":[120],"while":[121],"ambiguous":[122],"suppressed.":[125],"applied":[129],"here":[130],"track":[132],"ground":[133],"vehicles":[134],"thermal":[136],"airborne":[137],"video,":[138],"also":[141],"compared":[142],"other":[144],"state-of-the-art":[145],"algorithms.":[146]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":9},{"year":2012,"cited_by_count":12}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}