Dynamic Switching State Systems for Visual Tracking

By: Material type: ArticleArticleLanguage: English Publication details: Karlsruhe KIT Scientific Publishing 2020Description: 1 electronic resource (228 p.)ISBN:
  • KSP/1000122541
Subject(s): Online resources: 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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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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