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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| Auteur principal: | |
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| Format: | Électronique Chapitre de livre |
| Langue: | anglais |
| Publié: |
Karlsruhe
KIT Scientific Publishing
2020
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| Collection: | Karlsruher Schriften zur Anthropomatik
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| Sujets: | |
| Accès en ligne: | DOAB: download the publication DOAB: description of the publication |
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| Résumé: | 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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| Description matérielle: | 1 electronic resource (228 p.) |
| ISBN: | KSP/1000122541 |
| Accès: | Open Access |