Automatically recognizing strategic cooperative behaviors in various situations of a team sport

Motokazu Hojo, Keisuke Fujii, Yuki Inaba, Yoichi Motoyasu, Yoshinobu Kawahara

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Understanding multi-agent cooperative behavior is challenging in various scientific and engineering domains. In some cases, such as team sports, many cooperative behaviors can be visually categorized and labeled manually by experts. However, these actions which are manually categorized with the same label based on its function have low spatiotemporal similarity. In other words, it is difficult to find similar and different structures of the motions with the same and different labels, respectively. Here, we propose an automatic recognition system for strategic cooperative plays, which are the minimal, basic, and diverse plays in a ball game. Using player’s moving distance, geometric information, and distances among players, the proposed method accurately discriminated not only the cooperative plays in a primary area, i.e., near the ball, but also those distant from a primary area. We also propose a method to classify more detailed types of cooperative plays in various situations. The proposed framework, which sheds light on inconspicuous players to play important roles, could have a potential to detect well-defined and labeled cooperative behaviors.

Original languageEnglish
Article numbere0209247
JournalPloS one
Issue number12
Publication statusPublished - Dec 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General


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