Moving Object Tracking Based on Region Deformation and Background Modeling

We address the problem of tracking moving objects through a video sequence using deformable regions. So far, numerous tracking approaches based on region deformation have been proposed and the chosen region-based features are mostly special visual information such as mean-color and texture. This kind of information may be misleading in some dense visual clutter. In this paper, we present a temporal statistical tracking algorithm that extracts the region-based features via adaptive background mixture model. Our contribution is to define a new tracking criterion combining geometrical and motional features of the region. The resultant algorithm is expressed in the form of a level set partial differential equation. We also analyzed the adaptive ness of our algorithm. To further improve the computational efficiency, we introduce the narrowband method into our tracking scheme. Finally, highly promising experimental results are provided to illustrate the efficiency and accuracy of our method.