Good Features to Track
Shi, Jianbo; Tomasi, Carlo
No feature-based vision system can work until good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still an open problem. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, as well as a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under affine image transformations. We test performance with several simulations and experiments on real images.
computer science; technical report
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