Abstract
In the recent years, 3D tracking has gained attention due to the perforation of powerful computers and the increasing interest in tracking applications. One of the most common tracking algorithms used is the Kalman filter. Kalman filter is a linear estimator that is based on approximating system's dynamics using Gaussian probability distribution. In this paper, we provide a detailed evaluation of the most common Kalman filters, their use in the literature and their implementation for 3D visual tracking. The main types of Kalman filters discussed are linear Kalman filter, extended Kalman filer and unscented Kalman filter.
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