Browsing by Author "Hsieh, Hsin-Ying"

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ADVERSARIAL GENERATION OF DEFENSIVE TRAJECTORIES IN BASKETBALL GAMES
(2018-01-01)
Chen, Chieh-Yu
;
Lai, Wenze
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Hsieh, Hsin-Ying
;
Wang, Yu-Shuen
;
Peng, Wen-Hsiao
In this paper, we present a method to generate realistic trajectories of defensive players in a basketball game based on the ball and the offensive team's movements. We train on the NBA dataset a conditional generative adversarial network that learns spatio-temporal interactions between players' movements. The network consists of two components: a generator that takes as input a latent noise vector and the offensive team's trajectories to generate defensive trajectories; and a discriminator that evaluates the realistic degree of the generated results. Our system allows players and coaches to simulate how the opposing team will react to a newly developed offensive strategy for evaluating its effectiveness. Experimental results demonstrate the feasibility of the proposed algorithm.
BasketballGAN: Generating Basketball Play Simulation Through Sketching
(2019-01-01)
Hsieh, Hsin-Ying
;
Chen, Chieh-Yu
;
Wang, Yu-Shuen
;
Chuang, Jung-Hong
;
交大名義發表
We present a data-driven basketball set play simulation. Given an offensive set play sketch, our method simulates potential scenarios that may occur in the game. The simulation provides coaches and players with insights on how a given set play can be executed. To achieve the goal, we train a conditional adversarial network on NBA movement data to imitate the behaviors of how players move around the court through two major components: a generator that learns to generate natural player movements based on a latent noise and a user sketched set play; and a discriminator that is used to evaluate the realism of the basketball play. To improve the quality of simulation, we minimize 1.) a dribbler loss to prevent the ball from drifting away from the dribbler; 2.) a defender loss to prevent the dribbler from not being defended; 3.) a ball passing loss to ensure the straightness of passing trajectories; and 4) an acceleration loss to minimize unnecessary players' movements. To evaluate our system, we objectively compared real and simulated basketball set plays. Besides, a subjective test was conducted to judge whether a set play was real or generated by our network. On average, the mean correct rates to the binary tests were 56.17 %. Experiment results and the evaluations demonstrated the effectiveness of our system.
Generating Defensive Plays in Basketball Games
(2018-01-01)
Chen, Chieh-Yu
;
Lai, Wenze
;
Hsieh, Hsin-Ying
;
Zheng, Wen-Hao
;
Wang, Yu-Shuen
In this paper, we present a method to generate realistic defensive plays in a basketball game based on the ball and the offensive team's movements. Our system allows players and coaches to simulate how the opposing team will react to a newly developed offensive strategy for evaluating its effectiveness. To achieve the aim, we train on the NBA dataset a conditional generative adversarial network that learns spatio-temporal interactions between players' movements. The network consists of two components: a generator that takes a latent noise vector and the offensive team's trajectories as input to generate defensive team's trajectories; and a discriminator that evaluates the realistic degree of the generated results. Since a basketball game can be easily identified as fake if the ball handler, who is not defended, does not shoot the ball or cut into the restricted area, we add the wide open penalty to the objective function to assist model training. To evaluate the results, we compared the similarity of the real and the generated defensive plays, in terms of the players' movement speed and acceleration, distance to defend ball handlers and non-ball handlers, and the frequency of wide open occurrences. In addition, we conducted a user study with 59 participants for subjective tests. Experimental results show the high fidelity of the generated defensive plays to real data and demonstrate the feasibility of our algorithm.