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.

我们提出了一个神经网络 TTNet，旨在实时处理高分辨率的乒乓球视频，提供时间（事件检测）和空间（球检测和语义分割）数据，从而为自动裁判系统提供核心信息，同时我们发布了一个多任务数据集 OpenTTGames 以评估多任务方法，主要针对快速事件的检测和小物体跟踪，并在所呈现的数据集的测试部分展示了 97.0％的游戏事件检测的准确性，以及 2 像素 RMSE 的球检测准确性，并在单个消费级 GPU 的机器上实现每个输入张量不到 6 毫秒的推理时间，为实时多任务深度学习应用程序的发展做出贡献，并提供了一个潜在能够替代体育运动员的手动数据收集的方法，为裁判员的决策提供支持，并收集有关游戏过程的额外信息。