  The objective of this paper is to rectify any monocular image by computing a
homography matrix that transforms it to a bird's eye (overhead) view.
  We make the following contributions: (i) we show that the homography matrix
can be parameterised with only four parameters that specify the horizon line
and the vertical vanishing point, or only two if the field of view or focal
length is known; (ii) We introduce a novel representation for the geometry of a
line or point (which can be at infinity) that is suitable for regression with a
convolutional neural network (CNN); (iii) We introduce a large synthetic image
dataset with ground truth for the orthogonal vanishing points, that can be used
for training a CNN to predict these geometric entities; and finally (iv) We
achieve state-of-the-art results on horizon detection, with 74.52% AUC on the
Horizon Lines in the Wild dataset. Our method is fast and robust, and can be
used to remove perspective distortion from videos in real time.
