Improving Feature Tracking with Robust Statistics

This paper addresses robust feature tracking. The aim is to track point features in a sequence of images and to identify unreliable features resulting from occlusions, perspective distortions and strong intensity changes. We extend the well-known Shi–Tomasi–Kanade tracker by introducing an automatic scheme for rejecting spurious features. We employ a simple and efficient outliers rejection rule, called X84, and prove that its theoretical assumptions are satisfied in the feature tracking scenario. Experiments with real and synthetic images confirm that our algorithm consistently discards unreliable features; we show a quantitative example of the benefits introduced by the algorithm for the case of fundamental matrix estimation. The complete code of the robust tracker is available via ftp.