In this paper, our objective is to improve the performance of the existing
framework ShuttleNet in predicting badminton shot types and locations by
leveraging past strokes. We participated in the CoachAI Badminton Challenge at
IJCAI 2023 and achieved significantly better results compared to the baseline.
Ultimately, our team achieved the first position in the competition and we made
our code available.

通过利用过去的击球，本研究旨在提高现有框架 ShuttleNet 在预测羽毛球击球类型和位置方面的性能。我们参加了 IJCAI 2023 的 CoachAI 羽毛球挑战赛，并取得了与基准相比显著更好的结果。最终，我们的团队在比赛中获得了第一名，并提供了我们的代码。

2023 年 CoachAI 羽毛球挑战赛 AI 团队 8 号：先进的 ShuttleNet 技术用于球技预测

Team Intro to AI team8 at CoachAI Badminton Challenge 2023: Advanced  ShuttleNet for Shot Predictions

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.

介绍了一种新的用于击球预测的模型 ——ShuttleNet，该模型结合了比赛进展和运动员风格方面的信息，利用数据融合的方法提高击球预测的准确性，并在羽毛球数据集上取得了比较好的结果。

ShuttleNet: 羽毛球击球预测的位置感知式混合模型 —— 结合比赛进度和球员风格

ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles  for Stroke Forecasting in Badminton

Despite a lot of research efforts devoted in recent years, how to efficiently
learn long-term dependencies from sequences still remains a pretty challenging
task. As one of the key models for sequence learning, recurrent neural network
(RNN) and its variants such as long short term memory (LSTM) and gated
recurrent unit (GRU) are still not powerful enough in practice. One possible
reason is that they have only feedforward connections, which is different from
the biological neural system that is typically composed of both feedforward and
feedback connections. To address this problem, this paper proposes a
biologically-inspired deep network, called shuttleNet\footnote{Our code is
available at https://github.com/shiyemin/shuttlenet}. Technologically,
the shuttleNet consists of several processors, each of which is a GRU while
associated with multiple groups of cells and states. Unlike traditional RNNs,
all processors inside shuttleNet are loop connected to mimic the brain's
feedforward and feedback connections, in which they are shared across multiple
pathways in the loop connection. Attention mechanism is then employed to select
the best information flow pathway. Extensive experiments conducted on two
benchmark datasets (i.e UCF101 and HMDB51) show that we can beat
state-of-the-art methods by simply embedding shuttleNet into a CNN-RNN
framework.

本文提出了一种神经网络 shuttleNet，可以结合 CNN-RNN 框架来进行更有效的序列学习，其特点在于运用了循环反馈连接和注意力机制来模拟神经系统中的信息流动。