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A vision-based system to support tactical and physical analyses in futsal

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Abstract

This paper presents a vision-based system to support tactical and physical analyses of futsal teams. Most part of the current analyses in this sport are manually performed, while the existing solutions based on automatic approaches are frequently composed of costly and complex tools, developed for other kind of team sports, making it difficult their adoption by futsal teams. Our system, on the other hand, represents a simple yet efficient dedicated solution, which is based on the analyses of image sequences captured by a single stationary camera used to obtain top-view images of the entire court. We use adaptive background subtraction and blob analysis to detect players, as well as particle filters to track them in every video frame. The system determines the distance traveled by each player, his/her mean and maximum speeds, as well as generates heat maps that describe players’ occupancy during the match. To present the collected data, our system uses a specially developed mobile application. Experimental results with image sequences of an official match and a training match show that our system provides data with global mean tracking errors below 40 cm, demanding on 25 ms to process each frame and, thus, demonstrating its high application potential.

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Acknowledgements

The authors thank the support of CNPq under Processes 468042/2014-8 and 313163/2014-6, FAPEMIG under Process PPM-00542-15, CEFET-MG, CAPES and Minas Tênis Clube.

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Authors and Affiliations

  1. DECOM, CEFET-MG, Av. Amazonas, 7675, Belo Horizonte, MG, 30510-000, Brazil

    Pedro H. C. de Pádua, Flávio L. C. Pádua, Marconi de A. Pereira, Marco T. D. Sousa, Matheus B. de Oliveira & Elizabeth F. Wanner

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  1. Pedro H. C. de Pádua
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  2. Flávio L. C. Pádua
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  3. Marconi de A. Pereira
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  4. Marco T. D. Sousa
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  5. Matheus B. de Oliveira
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  6. Elizabeth F. Wanner
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Correspondence to Flávio L. C. Pádua.

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de Pádua, P.H.C., Pádua, F.L.C., de A. Pereira, M. et al. A vision-based system to support tactical and physical analyses in futsal. Machine Vision and Applications 28, 475–496 (2017). https://doi.org/10.1007/s00138-017-0849-z

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