{"id":"https://openalex.org/W3168705685","doi":"https://doi.org/10.1109/ieeeconf51394.2020.9443276","title":"Transfer Learning with Bayesian Filtering for Object Tracking Under Varying Conditions","display_name":"Transfer Learning with Bayesian Filtering for Object Tracking Under Varying Conditions","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W3168705685","doi":"https://doi.org/10.1109/ieeeconf51394.2020.9443276","mag":"3168705685"},"language":"en","primary_location":{"id":"doi:10.1109/ieeeconf51394.2020.9443276","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf51394.2020.9443276","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","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/A5075348456","display_name":"Omar Alotaibi","orcid":"https://orcid.org/0000-0002-4413-6810"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Omar Alotaibi","raw_affiliation_strings":["Arizona State University,School of Electrical, Computer and Energy Engineering,Tempe,AZ","School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ"],"affiliations":[{"raw_affiliation_string":"Arizona State University,School of Electrical, Computer and Energy Engineering,Tempe,AZ","institution_ids":["https://openalex.org/I55732556"]},{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069246416","display_name":"Antonia Papandreou\u2010Suppappola","orcid":"https://orcid.org/0000-0002-5223-6256"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Antonia Papandreou-Suppappola","raw_affiliation_strings":["Arizona State University,School of Electrical, Computer and Energy Engineering,Tempe,AZ","School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ"],"affiliations":[{"raw_affiliation_string":"Arizona State University,School of Electrical, Computer and Energy Engineering,Tempe,AZ","institution_ids":["https://openalex.org/I55732556"]},{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5075348456"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":0.2651,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66034108,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1523","last_page":"1527"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11220","display_name":"Water Systems and Optimization","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.72381591796875},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6353464126586914},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6216474771499634},{"id":"https://openalex.org/keywords/conjugate-prior","display_name":"Conjugate prior","score":0.6042552590370178},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.592811644077301},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5927080512046814},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5702511668205261},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5454933047294617},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5252724885940552},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5183547735214233},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.49365100264549255},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.4766731560230255},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.42014020681381226},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37618035078048706},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3701191246509552},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2913016974925995},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.14871275424957275}],"concepts":[{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.72381591796875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6353464126586914},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6216474771499634},{"id":"https://openalex.org/C26004113","wikidata":"https://www.wikidata.org/wiki/Q3711784","display_name":"Conjugate prior","level":4,"score":0.6042552590370178},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.592811644077301},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5927080512046814},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5702511668205261},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5454933047294617},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5252724885940552},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5183547735214233},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.49365100264549255},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.4766731560230255},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.42014020681381226},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37618035078048706},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3701191246509552},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2913016974925995},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14871275424957275},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf51394.2020.9443276","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf51394.2020.9443276","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W134197611","https://openalex.org/W1965998977","https://openalex.org/W2136097307","https://openalex.org/W2165698076","https://openalex.org/W2342102343","https://openalex.org/W2753228334","https://openalex.org/W2773192101","https://openalex.org/W2782304201","https://openalex.org/W2920067846","https://openalex.org/W3049131298","https://openalex.org/W6605365747","https://openalex.org/W6743669686"],"related_works":["https://openalex.org/W1544145018","https://openalex.org/W4307010368","https://openalex.org/W2789220062","https://openalex.org/W2732308154","https://openalex.org/W2534746541","https://openalex.org/W2422898642","https://openalex.org/W2375636617","https://openalex.org/W2352586926","https://openalex.org/W3177406559","https://openalex.org/W2736053408"],"abstract_inverted_index":{"We":[0],"propose":[1],"an":[2],"algorithm":[3,97],"that":[4],"integrates":[5],"Bayesian":[6],"filtering":[7],"with":[8,39],"transfer":[9],"learning":[10,37],"to":[11,23,71],"track":[12],"a":[13,86],"moving":[14],"object":[15],"under":[16],"unknown":[17],"time-varying":[18],"environmental":[19],"conditions.":[20],"In":[21],"order":[22],"account":[24],"for":[25,100],"measurement":[26,46,74],"noise":[27,105],"intensity":[28],"variations":[29],"in":[30],"the":[31,45,64,73,77,80,95],"primary":[32,78],"source,":[33,44],"we":[34],"use":[35],"multiple":[36,65],"sources":[38],"labeled":[40],"measurements.":[41],"For":[42],"each":[43],"likelihood":[47,75],"is":[48,98],"modeled":[49],"using":[50,85],"Gaussian":[51],"mixtures":[52],"whose":[53],"parameters":[54],"are":[55,68,83],"learned":[56,66,84],"from":[57],"conjugate":[58],"priors.":[59],"Weighted":[60],"basis":[61,81],"combinations":[62],"of":[63,76,94],"information":[67],"then":[69],"used":[70],"model":[72],"source;":[79],"weights":[82],"Dirichlet":[87],"distribution":[88],"prior.":[89],"The":[90],"improved":[91],"tracking":[92],"performance":[93],"proposed":[96],"demonstrated":[99],"both":[101],"low":[102],"and":[103],"high":[104],"scenarios.":[106]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}