ML Anthology

Good Features to Track

Jianbo Shi, Carlo Tomasi
CVPR 1994 pp. 593-600
doi:10.1109/CVPR.1994.323794 /cvpr/1994/shi1994cvpr-good/

Abstract

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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

CVPR Semantic Scholar

Cite

Text

Shi and Tomasi. "Good Features to Track." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1994. doi:10.1109/CVPR.1994.323794

Markdown

[Shi and Tomasi. "Good Features to Track." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1994.](https://mlanthology.org/cvpr/1994/shi1994cvpr-good/) doi:10.1109/CVPR.1994.323794

BibTeX

@inproceedings{shi1994cvpr-good,
  title     = {{Good Features to Track}},
  author    = {Shi, Jianbo and Tomasi, Carlo},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1994},
  pages     = {593-600},
  doi       = {10.1109/CVPR.1994.323794},
  url       = {https://mlanthology.org/cvpr/1994/shi1994cvpr-good/}
}