Optical flow-based probabilistic tracking

In this paper, we present an observation model to track objects using particle filter algorithms based on matching techniques for computing optical flow. Although optical flow information enables us to know the displacement of objects present in a scene, it cannot be used directly to displace an object model since flow calculation techniques lack the necessary precision. In view of the fact that probabilistic tracking algorithms enable imprecise or incomplete information to be handled naturally, this model is used as a natural means of incorporating flow information into the tracking.