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Bayesian Nonparametric Modeling and Inference for Multiple Object Tracking
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Bayesian Nonparametric Modeling and Inference for Multiple Object Tracking. |
| 개인저자 | Moraffah, Bahman. |
| 단체저자명 | Arizona State University. Electrical Engineering. |
| 발행사항 | [S.l.]: Arizona State University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 180 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03B. Dissertation Abstract International |
| ISBN | 9781085690911 |
| 학위논문주기 | Thesis (Ph.D.)--Arizona State University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Advisor: Papandreou-Suppappola, Antonia. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | 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 |
| 일반주제명 | Electrical engineering. Statistics. Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
