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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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Becker, S. 2020. Dynamic Switching State Systems for Visual Tracking. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000122541
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Veröffentlicht am 8. November 2020
Englisch
228
| Paperback | 978-3-7315-1038-3 |