Transfer Learning with Bayesian Filtering for Object Tracking under Varying Conditions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose an algorithm that integrates Bayesian filtering with transfer learning to track a moving object under unknown time-varying environmental conditions. In order to account for measurement noise intensity variations in the primary source, we use multiple learning sources with labeled measurements. For each source, the measurement likelihood is modeled using Gaussian mixtures whose parameters are learned from conjugate priors. Weighted basis combinations of the multiple learned information are then used to model the measurement likelihood of the primary source; the basis weights are learned using a Dirichlet distribution prior. The improved tracking performance of the proposed algorithm is demonstrated for both low and high noise scenarios.

Original languageEnglish (US)
Title of host publicationConference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1523-1527
Number of pages5
ISBN (Electronic)9780738131269
DOIs
StatePublished - Nov 1 2020
Event54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States
Duration: Nov 1 2020Nov 5 2020

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2020-November
ISSN (Print)1058-6393

Conference

Conference54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/TerritoryUnited States
CityPacific Grove
Period11/1/2011/5/20

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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