  We present a neural network TTNet aimed at real-time processing of
high-resolution table tennis videos, providing both temporal (events spotting)
and spatial (ball detection and semantic segmentation) data. This approach
gives core information for reasoning score updates by an auto-referee system.
  We also publish a multi-task dataset OpenTTGames with videos of table tennis
games in 120 fps labeled with events, semantic segmentation masks, and ball
coordinates for evaluation of multi-task approaches, primarily oriented on
spotting of quick events and small objects tracking. TTNet demonstrated 97.0%
accuracy in game events spotting along with 2 pixels RMSE in ball detection
with 97.5% accuracy on the test part of the presented dataset.
  The proposed network allows the processing of downscaled full HD videos with
inference time below 6 ms per input tensor on a machine with a single
consumer-grade GPU. Thus, we are contributing to the development of real-time
multi-task deep learning applications and presenting approach, which is
potentially capable of substituting manual data collection by sports scouts,
providing support for referees' decision-making, and gathering extra
information about the game process.
