Region-based mean shift tracking: Application to face tracking

We present a new technique for object tracking that is an extension of the mean shift tracking algorithm. The proposed technique relies on a segmentation of the area under analysis into a set of color-homogenous regions. The use of regions allows a robust estimation of the likelihood distributions that form the object and background models, as well as a precise shape definition of the object being tracked. Thanks to this accurate object definition, the object model can be updated through the tracking process, handling variations in the object representation. These concepts have been tested in the case of tracking human faces.