  In this work we generalize the plain MS trackers and attempt to overcome
standard mean shift trackers' two limitations.
  It is well known that modeling and maintaining a representation of a target
object is an important component of a successful visual tracker.
  However, little work has been done on building a robust template model for
kernel-based MS tracking. In contrast to building a template from a single
frame, we train a robust object representation model from a large amount of
data. Tracking is viewed as a binary classification problem, and a
discriminative classification rule is learned to distinguish between the object
and background. We adopt a support vector machine (SVM) for training. The
tracker is then implemented by maximizing the classification score. An
iterative optimization scheme very similar to MS is derived for this purpose.
