Using optical flow as evidence for probabilistic tracking

Abstract

In this paper, we present an observation model based on the Lucas and Kanade algorithm for computing optical flow, to track objects using particle filter algorithms. 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 has been used as a natural means of incorporating flow information into the tracking. © Springer-Verlag 2003.