Better features to track by estimating the tracking convergence region

Reliably tracking key points and textured patches from frame to frame is the basic requirement for many bottom-up computer vision algorithms. The problem of selecting the features that can be tracked well is addressed. The Lucas-Kanade tracking procedure is commonly used. We propose a method to estimate the size of the tracking procedure convergence region for each feature. The features that have a wider convergence region around them should be tracked better by the tracker. The size of the convergence region as a new feature goodness measure is compared with the widely accepted Shi-Tomasi feature selection criteria.