SURF Tracking

Most motion-based tracking algorithms assume that objects undergo rigid motion, which is most likely disobeyed in real world. In this paper, we present a novel motion-based tracking framework which makes no such assumptions. Object is represented by a set of local invariant features, whose motions are observed by a feature correspondence process. A generative model is proposed to depict the relationship between local feature motions and object global motion, whose parameters are learned efficiently by an on-line EM algorithm. And the object global motion is estimated in term of maximum likelihood of observations. Then an updating mechanism is employed to adapt object representation. Experiments show that our framework is flexible and robust in dealing with appearance changes, background clutter, illumination changes and occlusion.