CoachAI: A Project for Microscopic Badminton Match Data Collection and Tactical Analysis

Hsu, Tzu-Han; Wang, Chih-Chuan; Lin, Yuan-Hsiang; Chen, Ching-Hsuan; Jut, Nyan Ping; İk, Tsì-Uí; Peng, Wen-Chih; Wang, Yu-Shuen; Tsengt, Yu-Chee; Huang, Jiun-Long; Ching, Yu-Tai

doi:10.23919/apnoms.2019.8893039

Cited by 14 publications

(4 citation statements)

References 10 publications

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“…Hsu et al. [HCJ*19] proposed a badminton analysis and training pipeline, which incorporates object tracking with hardware devices, such as smart badminton rackets or smart gloves [SPHL20], to allow machines to interact with players and perform training in various tactics. Ghosh et al.…”

Section: Related Workmentioning

confidence: 99%

Exploration of Player Behaviours from Broadcast Badminton Videos

Chen

Shih

et al. 2023

Computer Graphics Forum

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Understanding an opposing player's behaviours and weaknesses is often the key to winning a badminton game. This study presents a system to extract game data from broadcast badminton videos, and visualize the extracted data to help coaches and players develop effective tactics. Specifically, we apply state‐of‐the‐art machine learning methods to partition a broadcast video into segments, in which each video segment shows a badminton rally. Next, we detect players' feet in each video frame and transform the player positions into the court coordinate system. Finally, we detect hit frames in each rally, in which the shuttle moves towards the opposite directions. By visualizing the extracted data, our system conveys when and where players hit the shuttle in historical games. Since players tend to smash or drop shuttles under a specific location, we provide users with interactive tools to filter data and focus on the distributions conditioned by player positions. This strategy also reduces visual clutter. Besides, our system plots the shuttle hitting distributions side‐by‐side, enabling visual comparison and analysis of player behaviours under different conditions. The results and the use cases demonstrate the feasibility of our system.

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Section: Related Workmentioning

confidence: 99%

Exploration of Player Behaviours from Broadcast Badminton Videos

Chen

Shih

et al. 2023

Computer Graphics Forum

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“…In this paper, we focus on turn-based sports and use badminton as the demonstration example. Various studies related to badminton have been carried out to collect badminton data, such as retrieving information from videos (Hsu et al 2019;Cao et al 2021;Yoshikawa et al 2021) or sensors (Chiu et al 2020;Steels et al 2020). Based on the collected data, several applications have been developed to analyze tactic performance (Wang 2022), e.g., shot influence (Wang et al 2021(Wang et al , 2022a), an analysis system (Chu et al 2022), and stroke forecasting (Wang et al 2022b).…”

Section: Introductionmentioning

confidence: 99%

Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badminton

Chang

Wang

Peng

2023

AAAI

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Sports analytics has captured increasing attention since analysis of the various data enables insights for training strategies, player evaluation, etc. In this paper, we focus on predicting what types of returning strokes will be made, and where players will move to based on previous strokes. As this problem has not been addressed to date, movement forecasting can be tackled through sequence-based and graph-based models by formulating as a sequence prediction task. However, existing sequence-based models neglect the effects of interactions between players, and graph-based models still suffer from multifaceted perspectives on the next movement. Moreover, there is no existing work on representing strategic relations among players' shot types and movements. To address these challenges, we first introduce the procedure of the Player Movements (PM) graph to exploit the structural movements of players with strategic relations. Based on the PM graph, we propose a novel Dynamic Graphs and Hierarchical Fusion for Movement Forecasting model (DyMF) with interaction style extractors to capture the mutual interactions of players themselves and between both players within a rally, and dynamic players' tactics across time. In addition, hierarchical fusion modules are designed to incorporate the style influence of both players and rally interactions. Extensive experiments show that our model empirically outperforms both sequence- and graph-based methods and demonstrate the practical usage of movement forecasting. Code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/Movement%20Forecasting.

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“…In this paper, we focus on turn-based sports and use badminton as the demonstration example. The related works on badminton mainly focus on quantifying stroke performance (Sharma et al 2021;Wang et al 2021) or detecting strokerelated information from videos (Chu and Situmeang 2017;Hsu et al 2019;Wang et al 2020;Yoshikawa et al 2021). However, there is another application that has not yet been addressed by previous works: to forecast the future strokes including shot types and locations given the past stroke sequences.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Shuai

Chang

et al. 2022

AAAI

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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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CoachAI: A Project for Microscopic Badminton Match Data Collection and Tactical Analysis

Cited by 14 publications

References 10 publications

Exploration of Player Behaviours from Broadcast Badminton Videos

Exploration of Player Behaviours from Broadcast Badminton Videos

Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badminton

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

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