Visual tracking, a building block for many applications, has challenges such as occlusions, illumination changes, background clutter and variable motion dynamics that may degrade the tracking performance and are likely to cause failures. In this presentation, Track-Evaluate-Correct (self-correlation) framework will be discussed in order to achieve a robust tracking over existing trackers. For existing trackers, the framework integrates an evaluation unit to check the status of tracking quality online and a correction unit to avoid upcoming failures or to recover from failures. A Dynamic Bayesian Network (DBN) model will be described for a generic representation of the self-correcting tracking and particular trackers developed using the Track-Evaluate-Correct framework will be discussed.?