  The paper describes a deep neural network-based detector dedicated for ball
and players detection in high resolution, long shot, video recordings of soccer
matches. The detector, dubbed FootAndBall, has an efficient fully convolutional
architecture and can operate on input video stream with an arbitrary
resolution. It produces ball confidence map encoding the position of the
detected ball, player confidence map and player bounding boxes tensor encoding
players' positions and bounding boxes. The network uses Feature Pyramid Network
desing pattern, where lower level features with higher spatial resolution are
combined with higher level features with bigger receptive field. This improves
discriminability of small objects (the ball) as larger visual context around
the object of interest is taken into account for the classification. Due to its
specialized design, the network has two orders of magnitude less parameters
than a generic deep neural network-based object detector, such as SSD or YOLO.
This allows real-time processing of high resolution input video stream. Our
code and pre-trained model can be found on the project website:
https://github.com/jac99/FootAndBall .
