An Approach to Use Deep Learning to Automatically Recognize Team Tactics in Team Ball Games

Friedemann Schwenkreis

Abstract

Deep Learning methods are used successfully in pattern recognition areas like face or voice recognition. However, the recognition of sequences of images for automatically recognizing tactical movements in team sports is still an unsolved area. This paper introduces an approach to solve this class of problems by mapping the sequence problem onto the classical shape recognition problem in case of pictures. Using team handball as an example, the paper first introduces the underlying data collection approach and a corresponding data model before introducing the actual mapping onto classical deep learning approaches. Team handball is just used as an example sport to illustrate the concept, which can be applied to any team ball game in which coordinated team moves are used.

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Paper Citation


in Harvard Style

Schwenkreis F. (2018). An Approach to Use Deep Learning to Automatically Recognize Team Tactics in Team Ball Games.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 157-162. DOI: 10.5220/0006823901570162


in Bibtex Style

@conference{data18,
author={Friedemann Schwenkreis},
title={An Approach to Use Deep Learning to Automatically Recognize Team Tactics in Team Ball Games},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2018},
pages={157-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006823901570162},
isbn={978-989-758-318-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - An Approach to Use Deep Learning to Automatically Recognize Team Tactics in Team Ball Games
SN - 978-989-758-318-6
AU - Schwenkreis F.
PY - 2018
SP - 157
EP - 162
DO - 10.5220/0006823901570162