Abstract
Sports analytics are on the rise in European football, however, due to the high cost so far only the top tier leagues and championships have had the privilege of collecting high precision data to build upon. We believe that this opportunity should be available for everyone especially for youth teams, to develop and recognize talent earlier. We therefore set the goal of creating a low-cost player tracking system that could be applied in a wide base of football clubs and pitches, which in turn would widen the reach for sports analytics, ultimately assisting the work of scouts and coaches in general. In this paper, we present a low-cost optical tracking solution based on cheap action cameras and cloud-deployed data processing. As we build on existing research results in terms of methods for player detection, i.e., background-foreground separation, and for tracking, i.e., Kalman filter, we adapt those algorithms with the aim of sacrificing as least as possible on accuracy while keeping costs low. The results are promising: our system yields significantly better accuracy than a standard deep learning based tracking model at the fraction of its cost. In fact, at a cost of $2.4 per match spent on cloud processing of videos for real-time results, all players can be tracked with a 11-meter precision on average.
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Acknowledgement
This work was supported by the National Research, Development and Innovation Office of Hungary (NKFIH) in research project FK 128233, financed under the FK_18 funding scheme.
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Csanalosi, G., Dobreff, G., Pasic, A., Molnar, M., Toka, L. (2020). Low-Cost Optical Tracking of Soccer Players. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds) Machine Learning and Data Mining for Sports Analytics. MLSA 2020. Communications in Computer and Information Science, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-64912-8_3
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