Real-time camera tracking using a particle filter combined with unscented Kalman filters

Doppler and bearing tracking using fuzzy adaptive unscented Kalman filter

The topic of Doppler and Bearing Tracking (DBT) problem is to achieve a target trajectory using the Doppler and Bearing measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. Several techniques were studied to deal with this topic, such as the unscented Kalman filter. Nevertheless, the performance of the filter depends directly on the...

Real-Time Camera Tracking Using a Particle Filter

We describe a particle filtering method for vision based tracking of a hand held calibrated camera in real-time. The ability of the particle filter to deal with non-linearities and non-Gaussian statistics suggests the potential to provide improved robustness over existing approaches, such as those based on the Kalman filter. In our approach, the particle filter provides recursive approximations...

Real-Time tracking system using Single-Constraints-At-A-Time and Unscented Kalman Filter

Parametric contour tracking using unscented Kalman filter

This paper presents an efficient method to integrate various spatial-temporal constraints to regularize the contour tracking. Specifically, the global shape prior, contour smoothness and object dynamics are addressed. First, the contour is represented as a parametric shape, based on which a causal smoothness constraint can be developed to exploit the local spatial constraint. The causality natu...

Initializing An Unscented Kalman Filter Using A Particle Filter

This work develops an algorithm to initialize an Unscented Kalman Filter using a Particle Filter for applications with initial non-Gaussian probability density functions. The method is applied to estimating the position of a road vehicle along a one-mile test track using terrain-based localization where the pitch response of the vehicle is compared to a premeasured pitch map of the test track. ...