Good features to track

Published

Journal Article

No feature-based vision system can work unless 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 hard. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, and 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.

Duke Authors

Cited Authors

  • Shi, J; Tomasi, C

Published Date

  • January 1, 1994

Published In

Start / End Page

  • 593 - 600

International Standard Serial Number (ISSN)

  • 1063-6919

Citation Source

  • Scopus