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Low Cost Player Tracking in Field Hockey

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Machine Learning and Data Mining for Sports Analytics (MLSA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1571))

Abstract

In the paper, we describe the technical details of a multi-player tracker system using tracking data obtained from a single low-cost stationary camera on field hockey games. Analyzing the tracking data of the players only from the transmitted video opens a multitude of applications that allows the cost of technology to be reduced. This method does not depend on the cooperation of the players (by using sensors) or their teams (by sharing data with a third party). The approach taken in this paper uses a variety of computer vision and tracking techniques. Making player tracking data more accessible lowers the barrier to entry for sports research and increases the period during which advanced analysis methods can be applied. The proposed system runs the full pipeline at 3 fps on a computer with a simple graphics card.

This work was funded by the DAIQUIRI project, cofunded by imec, a research institute founded by the Flemish Government. Project partners are Ghent University, InTheRace, Arinti, Cronos, VideoHouse, NEP Belgium, and VRT, with project support from VLAIO under grant number HBC.2019.0053.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Antwerpen - imec, IDLab, Antwerpen, Belgium

    Henrique Duarte Moura, Leonid Kholkine, Laurens Van Damme, Kevin Mets, Christiaan Leysen, Tom De Schepper, Peter Hellinckx & Steven Latré

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  1. Henrique Duarte Moura
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  2. Leonid Kholkine
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  6. Tom De Schepper
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  8. Steven Latré
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Corresponding author

Correspondence to Henrique Duarte Moura .

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Editors and Affiliations

  1. Leuphana University of Lüneburg, Lüneburg, Germany

    Ulf Brefeld

  2. Katholieke Universiteit Leuven, Leuven, Belgium

    Jesse Davis

  3. Katholieke Universiteit Leuven, Leuven, Belgium

    Jan Van Haaren

  4. University of Caen Normandie, Caen, France

    Albrecht Zimmermann

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Duarte Moura, H. et al. (2022). Low Cost Player Tracking in Field Hockey. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds) Machine Learning and Data Mining for Sports Analytics. MLSA 2021. Communications in Computer and Information Science, vol 1571. Springer, Cham. https://doi.org/10.1007/978-3-031-02044-5_9

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  • DOI: https://doi.org/10.1007/978-3-031-02044-5_9

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