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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Bibliografski detalji
Glavni autor: Becker, Stefan (auth)
Format: Elektronički Poglavlje knjige
Jezik:engleski
Izdano: Karlsruhe KIT Scientific Publishing 2020
Serija:Karlsruher Schriften zur Anthropomatik
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Opis
Sažetak: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.
Opis fizičkog objekta:1 electronic resource (228 p.)
ISBN:KSP/1000122541
Pristup:Open Access