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PLoS One. 2018 Dec 18;13(12):e0209247. doi: 10.1371/journal.pone.0209247. eCollection 2018.

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

Author information

1
RIKEN Center for Advanced Intelligence Project, Osaka, Japan.
2
Japanese Institute of Sports Sciences, Tokyo, Japan.
3
Tokuyama University, Yamaguchi, Japan.
4
The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan.

Abstract

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.

PMID:
30562367
PMCID:
PMC6298668
DOI:
10.1371/journal.pone.0209247
[Indexed for MEDLINE]
Free PMC Article

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