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Dynamic Switching State Systems for Visual Tracking
Abstract
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- doc-type:doctoralThesis
- Text
- info:eu-repo/semantics/doctoralThesis
- dissertation
- info:eu-repo/semantics/publishedVersion
- Zustandsschätzung
- videobasierte Objektverfolgung
- Trajektorienprädiktion
- state estimation
- visual tracking
- trajectory prediction
- ddc:004
- DATA processing & computer science
- info:eu-repo/classification/ddc/004
Similar works
This paper was published in KITopen.
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