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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| Main Author: | |
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| Format: | Electronic Book Chapter |
| Language: | English |
| Published: |
Karlsruhe
KIT Scientific Publishing
2020
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| Series: | Karlsruher Schriften zur Anthropomatik
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| Subjects: | |
| Online Access: | DOAB: download the publication DOAB: description of the publication |
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| 100 | 1 | |a Becker, Stefan |4 auth | |
| 245 | 1 | 0 | |a Dynamic Switching State Systems for Visual Tracking |
| 260 | |a Karlsruhe |b KIT Scientific Publishing |c 2020 | ||
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| 520 | |a 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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| 546 | |a English | ||
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| 653 | |a videobasierte Objektverfolgung | ||
| 653 | |a state estimation | ||
| 653 | |a visual tracking | ||
| 653 | |a trajectory prediction | ||
| 653 | |a Trajektorienpradiktion | ||
| 653 | |a Zustandsschatzung | ||
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