Today I read a paper titled “Generalized Kernel-based Visual Tracking”
The abstract is:
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.