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Bayesian Nonparametric Modeling and Inference for Multiple Object Tracking

Abstract The problem of multiple object tracking seeks to jointly estimate the time-varying cardinality and trajectory of each object. There are numerous challenges that are encountered in tracking multiple objects including a time-varying number of measurements, under varying constraints, and environmental conditions. In this thesis, the proposed statistical methods integrate the use of physical-based models with Bayesian nonparametric methods to address the main challenges in a tracking problem. In particular, Bayesian nonparametric methods are exploited to efficiently and robustly infer object identity and learn time-dependent cardinality; together with Bayesian inference methods, they are also used to associate measurements to objects and estima... (more)
Created Date 2019
Contributor Moraffah, Bahman (Author) / Papandreou-Suppappola, Antonia (Advisor) / Bliss, Daniel W. (Committee member) / Richmond, Christ D. (Committee member) / Dasarathy, Gautam (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Statistics / Computer science / Bayesian Nonparametrics / Dependent Dirichlet Process / Dependent Pitman-Yor Process / Multiple Object Tracking / Scalable Bayesian Inference
Type Doctoral Dissertation
Extent 179 pages
Language English
Note Doctoral Dissertation Electrical Engineering 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis