Algorithmic Fusion for More Robust Feature Tracking

We present a framework for merging the results of independent featurebased 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.

• Type: Journal
• Year: 2002
• Where: IJCV
• Authors: Brendan McCane, Ben Galvin, Kevin Novins
• Keywords: Classification Based Approach, Corner Trackers, Featurebased Motion Trackers, IJCV 2002