Measurement and Performance Evaluation of Lob Technique Using Aerodynamic Model in Badminton Matches

Shen, Lejun; Zhang, Hui; Zhu, Min; Zheng, Jia; Ren, Yawei

doi:10.1007/978-3-030-35048-2_7

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…3D reconstruction is a widely studied topic in vision [27], however, its use in sports videos is relatively recent. [24,25] showed a confirming point method that can reconstruct 3D shuttlecock positions but requires human intervention in placing the confirming points for every shot. ShuttleSpace showed that 3D trajectories of badminton shots is useful for top-level players in an immersive analytics system [35].…”

Section: Related Workmentioning

confidence: 99%

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MonoTrack: Shuttle trajectory reconstruction from monocular badminton video

Liu¹,

Wang²

2022

Preprint

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Trajectory estimation is a fundamental component of racket sport analytics, as the trajectory contains information not only about the winning and losing of each point, but also how it was won or lost. In sports such as badminton, players benefit from knowing the full 3D trajectory, as the height of shuttlecock or ball provides valuable tactical information. Unfortunately, 3D reconstruction is a notoriously hard problem, and standard trajectory estimators can only track 2D pixel coordinates. In this work, we present the first complete end-to-end system for the extraction and segmentation of 3D shuttle trajectories from monocular badminton videos. Our system integrates badminton domain knowledge such as court dimension, shot placement, physical laws of motion, along with vision-based features such as player poses and shuttle tracking. We find that significant engineering efforts and model improvements are needed to make the overall system robust, and as a by-product of our work, improve state-of-the-art results on court recognition, 2D trajectory estimation, and hit recognition.

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Section: Related Workmentioning

confidence: 99%

“…Altogether, this results in limited success achieved in prior work. For example, the method proposed by [24,25] requires human intervention for every shot. In contrast, our system requires no human intervention and can significantly outperform the model-based work by [19].…”

Section: Introductionmentioning

confidence: 99%

MonoTrack: Shuttle trajectory reconstruction from monocular badminton video

Liu¹,

Wang²

2022

Preprint

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“…In racket sports, most previous studies on player evaluation were limited to traditional methods. Shen et al evaluate a single shot type (lob) in badminton, and defined a good/bad lob on whether the subsequent action of the opponent is a smash and leads to a score [2]. This work was considered in the limited available research; thus, inspired by this idea, we adopt the Markov model approach (i.e., the second approach in the above paragraph) using technical whole-body movement and tactical information for player evaluation in actual games.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning in a Racket Sport for Player Evaluation With Technical and Tactical Contexts

2022

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Evaluating the performance of players in a dynamic competition is vital for achieving effective sports coaching. However, a quantitative evaluation of players in racket sports is difficult because it is derived from the integration of complex tactical and technical (i.e., whole-body movement) performances. In this study, we propose a new evaluation method for racket sports based on deep reinforcement learning, which can analyze the motion of a player in more detail, rather than only considering the results (i.e., scores). Our method uses historical data including information related to the tactical and technical performance of players to learn the next-score probability as a Q-function, which is used to value the actions of the players. We leverages long short-term memory model for the learning of Q-function with the poses of the players and the position of the shuttlecock as the input, which are identified by the AlphaPose and TrackNet algorithms, respectively. We verified our approach by comparing various baselines and demonstrated the effectiveness of our method through use cases that analyze the performance of the top badminton players in world-class events.INDEX TERMS deep reinforcement learning, sports analytics, Q values, action evaluation.

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