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.
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- Electrical Engineering
- Computer Science
- Bayesian Nonparametrics
- Dependent Dirichlet Process
- Dependent Pitman-Yor Process
- Multiple Object Tracking
- Scalable Bayesian Inference
- Nonparametric statistics
- Bayesian statistical decision theory
- Tracking (Engineering)--Statistical methods.
- Tracking (Engineering)
- Partial requirement for: Ph.D., Arizona State University, 2019Note typethesis
- Includes bibliographical references (pages 154-162)Note typebibliography
- Field of study: Electrical engineering