Algorithmic fusion for more robust feature tracking
We present a framework for merging the results of independent feature-based motion trackers using a classification based approach. We demonstrate the efficacy of the framework using corner trackers as an example. The major problem with such systems is generating ground truth data for training. We show how synthetic data can be used effectively to overcome this problem. Our combined system performs better in both dropouts and errors than a correspondence tracker, and had less than half the dropouts at the cost of moderate increase in error compared to a relaxation tracker.
algorithmic fusion feature tracking motion analysis combining multiple trackers