  A novel Bayesian framework is proposed, which explicitly relates the
homography of one video frame to the next through an affine transformation
while explicitly modelling keypoint uncertainty. The literature has previously
used differential homography between subsequent frames, but not in a Bayesian
setting. In cases where Bayesian methods have been applied, camera motion is
not adequately modelled, and keypoints are treated as deterministic. The
proposed method, Bayesian Homography Inference from Tracked Keypoints (BHITK),
employs a two-stage Kalman filter and significantly improves existing methods.
Existing keypoint detection methods may be easily augmented with BHITK. It
enables less sophisticated and less computationally expensive methods to
outperform the state-of-the-art approaches in most homography evaluation
metrics. Furthermore, the homography annotations of the WorldCup and
TS-WorldCup datasets have been refined using a custom homography annotation
tool that has been released for public use. The refined datasets are
consolidated and released as the consolidated and refined WorldCup (CARWC)
dataset.
