$H$-RANSAC, an algorithmic variant for Homography image transform from
  featureless point sets: application to video-based football analytics
George Nousias Konstantinos Delibasis Ilias Maglogiannis
Estimating homography matrix between two images has various applications like
image stitching or image mosaicing and spatial information retrieval from
multiple camera views, but has been proved to be a complicated problem,
especially in cases of radically different camera poses and zoom factors. Many
relevant approaches have been proposed, utilizing direct feature based, or deep
learning methodologies. In this paper, we propose a generalized RANSAC
algorithm, H-RANSAC, to retrieve homography image transformations from sets of
points without descriptive local feature vectors and point pairing. We allow
the points to be optionally labelled in two classes. We propose a robust
criterion that rejects implausible point selection before each iteration of
RANSAC, based on the type of the quadrilaterals formed by random point pair
selection (convex or concave and (non)-self-intersecting). A similar post-hoc
criterion rejects implausible homography transformations is included at the end
of each iteration. The expected maximum iterations of $H$-RANSAC are derived
for different probabilities of success, according to the number of points per
image and per class, and the percentage of outliers. The proposed methodology
is tested on a large dataset of images acquired by 12 cameras during real
football matches, where radically different views at each timestamp are to be
matched. Comparisons with state-of-the-art implementations of RANSAC combined
with classic and deep learning image salient point detection indicates the
superiority of the proposed $H$-RANSAC, in terms of average reprojection error
and number of successfully processed pairs of frames, rendering it the method
of choice in cases of image homography alignment with few tens of points, while
local features are not available, or not descriptive enough. The implementation
of $H$-RANSAC is available in https://github.com/gnousias/H-RANSAC