Adversarial generation of defensive trajectories in basketball games

Chieh Yu Chen, Wenze Lai, Hsin Ying Hsieh, Yu-Shuen Wang, Wen-Hsiao Peng, Jung-Hong Chuang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538641958
DOIs
StatePublished - 28 Nov 2018
Event2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018 - San Diego, United States
Duration: 23 Jul 201827 Jul 2018

Publication series

Name2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018

Conference

Conference2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018
CountryUnited States
CitySan Diego
Period23/07/1827/07/18

Keywords

  • basketball
  • Conditional adversarial network
  • strategies

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