標題: Generating Defensive Plays in Basketball Games
作者: Chen, Chieh-Yu
Lai, Wenze
Hsieh, Hsin-Ying
Zheng, Wen-Hao
Wang, Yu-Shuen
Chuang, Jung-Hong
交大名義發表
National Chiao Tung University
關鍵字: Conditional adversarial network;basketball;defensive strategies
公開日期: 1-Jan-2018
摘要: 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.
URI: http://dx.doi.org/10.1145/3240508.3240670
http://hdl.handle.net/11536/153840
ISBN: 978-1-4503-5665-7
DOI: 10.1145/3240508.3240670
期刊: PROCEEDINGS OF THE 2018 ACM MULTIMEDIA CONFERENCE (MM'18)
起始頁: 1580
結束頁: 1588
Appears in Collections:Conferences Paper