Weighting observations: the use of kinematic models in object tracking

We describe a model-based object tracking system that updates the configuration parameters of an object model based upon information gathered from a sequence of monocular images. Realistic object and imaging models are used to determine the expected visibility of object features, and to determine the expected appearance of all visible features. We formulate the tracking problem as one of parameter estimation from partially observed data, and apply the extended Kalman filtering (EKF) algorithm. The models are also used to determine what point feature movement reveals about the configuration parameters of the object. This information is used by the EKF to update estimates for parameters, and for the uncertainty in the current estimates, based on observations of point features in monocular images.