Dynamic Switching State Systems for Visual Tracking / / Stefan Becker.
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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| Place / Publishing House: | Karlsruhe : : KIT Scientific Publishing,, 2020. |
| Year of Publication: | 2020 |
| Language: | English |
| Series: | Karlsruher Schriften zur Anthropomatik
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| Physical Description: | 1 online resource (228 pages) :; illustrations. |
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| Summary: | 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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| Hierarchical level: | Monograph |
| Statement of Responsibility: | Stefan Becker. |