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Titel:

Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for Pose Regularization

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Coskun, H.; Achilles, F.; DiPietro, R.; Navab, N.; Tombari, F.
Abstract:
One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used for regularization. One of the most widely-used methods is the Kalman filter, which is both extremely simple and general. However, Kalman filters require a motion model and measurement model to be specified a priori, which burdens the modeler and simultaneously demands that we use explicit models that are often only c...     »
Stichworte:
CAMP,CAMPComputerVision,ComputerVision,ICCV,ICCV2017,KALMAN
Jahr:
2017
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