Measurement error estimation for feature tracking

Performance estimation for feature tracking is a critical issue, if feature tracking results are to be used intelligently. In this paper, we derive quantitative measures for the spatial accuracy of a particular feature tracker. This method uses the results from the sum-of-squared-differences correlation measure commonly used for feature tracking to estimate the accuracy (in the image plane) of the feature tracking result. In this way, feature tracking results can be analyzed and exploited to a greater extent without placing undue confidence in inaccurate results or throwing out accurate results. We argue that this interpretation of results is more flexible and useful than simply using a confidence measure on tracking results to accept or reject features. For example, an extended Kalman filtering framework can assimilate these tracking results directly to monitor the uncertainty in the estimation process for the state of an articulated object.