Estimating uncertainty in SSD-based feature tracking

Sum-of-squared-differences (SSD) based feature trackers have enjoyed growing popularity in recent years, particularly in the field of visual servo control of robotic manipulators. These trackers use SSD correlation measures to locate target features in sequences of images. The results can then be used to estimate the motion of objects in the scene, to infer the 3D structure of the scene, or to control robot motions. The reliability of the information provided by these trackers can be degraded by a variety of factors, including changes in illumination, poor image contrast, occlusion of features, or unmodeled changes in objects. This has led other researchers to develop confidence measures that are used to either accept or reject individual features that are located by the tracker. In this paper, we derive quantitative measures for the spatial uncertainty of the results provided by SSD-based feature trackers. Unlike previous confidence measures that have been used only to accept or reject hypotheses, our new measure allows the uncertainty associated with a feature to be used to weight its influence on the overall tracking process. Specifically, we scale the SSD correlation surface, fit a Gaussian distribution to this surface, and use this distribution to estimate values for a covariance matrix. We illustrate the efficacy of these measures by showing the performance of an example object tracking system with and without the measures.