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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| Format: | eBook |
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Karlsruhe
KIT Scientific Publishing
2020
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| Online Access: | Get fulltext |
| 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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| Physical Description: | 1 electronic resource (228 p.) |
| ISBN: | KSP/1000122541 |
| Access: | Open Access |