Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICMEW.2018.8551533 | 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) |
Keywords | Field | DocType |
Conditional adversarial network,basketball,strategies | Computer vision,Discriminator,Generative adversarial network,Computer science,Basketball games,Artificial intelligence,Basketball,Adversarial system,Offensive | Conference |
ISSN | ISBN | Citations |
2330-7927 | 978-1-5386-4196-5 | 1 |
PageRank | References | Authors |
0.48 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chieh-Yu Chen | 1 | 1 | 1.16 |
| Wenze Lai | 2 | 1 | 0.82 |
| Hsin-Ying Hsieh | 3 | 1 | 1.16 |
| Yu-Shuen Wang | 4 | 785 | 39.72 |
| Wen-Hsiao Peng | 5 | 209 | 33.15 |
| Jung-hong Chuang | 6 | 136 | 19.62 |