Good Features to Track (1994) [758 citations — 9 self]
http://www.ri.cmu.edu/pub_files/pub2/shi_jianbo_19
http://www.cis.upenn.edu/~jshi/papers/good-feature
http://www.cs.cmu.edu/~jshi/papers/good-features.p
http://vision.stanford.edu/public/publication/shi/
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author = {Jianbo Shi and Carlo Tomasi},
title = {Good Features to Track},
booktitle = {},
year = {1994},
pages = {593--600}
}
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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. 1 Introduction IEEE Conference on Computer Vision and Pattern Recognition (CVPR94) Seattle, June 1994 Is feature tracking a solved problem? The extensive studies of image correlation [4], [3], [15], [18], [7], [17] and sum-of-squared-difference (SSD) m...
