Parameter Estimation for Visual Tracking of a Spherical Pendulum with Particle Filter

In this paper we present a particle filter for visual tracking using a physical model of the object to be tracked. Moreover, a parameter estimation scheme is implemented to identify the physical parameters of the dynamic model. This is based on recently developed methods for online estimation of static parameters using stochastic gradient search methods. The use of a dynamic model to compute the particle filter prior gives improved tracking accuracy and reduces the required noise level in the model. This makes it possible to predict the motion of the object for use in robotic applications. The performance of the method is validated in experiments with visual tracking of a free swinging pendulum of the type used in robotic loading of objects for automatic paint lines.