Good Features to Track
Conference Paper, Proceedings of (CVPR) Computer Vision and Pattern Recognition, pp. 593 - 600, June, 1994
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.
BibTeX
@conference{Shi-1994-16087,author = {Jianbo Shi and Carlo Tomasi},
title = {Good Features to Track},
booktitle = {Proceedings of (CVPR) Computer Vision and Pattern Recognition},
year = {1994},
month = {June},
pages = {593 - 600},
}
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