TTNet: Real-time temporal and spatial video analysis of table tennis

• URL: http://arxiv.org/abs/2004.09927v1
• Date: Tue, 21 Apr 2020 11:57:51 GMT
• Title: TTNet: Real-time temporal and spatial video analysis of table tennis
• Authors: Roman Voeikov, Nikolay Falaleev and Ruslan Baikulov
• Abstract summary: We present a neural network aimed at real-time processing of high-resolution table tennis videos. This approach gives core information for reasoning score updates by an auto-referee system. We publish a multi-task dataset OpenTTGames with videos of table tennis games in 120 fps labeled with events.
• Score: 5.156484100374058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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