Dynamic Switching State Systems for Visual Tracking
This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought t...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Karlsruhe :
KIT Scientific Publishing
2020.
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| Colección: | Karlsruher Schriften zur Anthropomatik
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009871124506719 |
| Sumario: | This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together. |
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| Descripción Física: | 1 online resource (228 pages) : illustrations |
