@inproceedings{1b2310d355154ae795628e1a1b2e8dc9,
title = "Adversarial generation of defensive trajectories in basketball games",
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.",
keywords = "Conditional adversarial network, basketball, strategies",
author = "Chen, {Chieh Yu} and Wenze Lai and Hsieh, {Hsin Ying} and Yu-Shuen Wang and Wen-Hsiao Peng and Jung-Hong Chuang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018 ; Conference date: 23-07-2018 Through 27-07-2018",
year = "2018",
month = jul,
day = "23",
doi = "10.1109/ICMEW.2018.8551533",
language = "English",
series = "2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018",
address = "美國",
}