ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles
for Stroke Forecasting in Badminton
- URL: http://arxiv.org/abs/2112.01044v1
- Date: Thu, 2 Dec 2021 08:14:23 GMT
- Title: ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles
for Stroke Forecasting in Badminton
- Authors: Wei-Yao Wang, Hong-Han Shuai, Kai-Shiang Chang, Wen-Chih Peng
- Abstract summary: This paper focuses on objectively judging what and where to return strokes in turn-based sports.
We propose a novel Position-aware Fusion of Rally Progress and Player Styles framework (ShuttleNet) that incorporates rally progress and information of the players.
- Score: 18.524164548051417
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The increasing demand for analyzing the insights in sports has stimulated a
line of productive studies from a variety of perspectives, e.g., health state
monitoring, outcome prediction. In this paper, we focus on objectively judging
what and where to return strokes, which is still unexplored in turn-based
sports. By formulating stroke forecasting as a sequence prediction task,
existing works can tackle the problem but fail to model information based on
the characteristics of badminton. To address these limitations, we propose a
novel Position-aware Fusion of Rally Progress and Player Styles framework
(ShuttleNet) that incorporates rally progress and information of the players by
two modified encoder-decoder extractors. Moreover, we design a fusion network
to integrate rally contexts and contexts of the players by conditioning on
information dependency and different positions. Extensive experiments on the
badminton dataset demonstrate that ShuttleNet significantly outperforms the
state-of-the-art methods and also empirically validates the feasibility of each
component in ShuttleNet. On top of that, we provide an analysis scenario for
the stroke forecasting problem.
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