Research Article
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Year 2022, Volume 10, Issue 1, 1 - 8, 01.01.2022
https://doi.org/10.21541/apjess.1060725

Abstract

References

  • A. Bialkowski, P. Lucey, P. Carr, Y. Yue, S. Sridharan, and I. Matthews, "Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data," Proc. - IEEE Int. Conf. Data Mining, ICDM, vol. 2015-Janua, no. January, pp. 725–730, 2014.
  • B. Skinner and S. J. Guy, "A method for using player tracking data in basketball to learn player skills and predict team performance," PLoS One, vol. 10, no. 9, pp. 1–15, 2015.
  • P. Lucey, D. Oliver, P. Carr, J. Roth, and I. Matthews, "Assessing team strategy using spatiotemporal data," Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. Part F1288, pp. 1366–1374, 2013.
  • C. Perin, R. Vuillemot, C. D. Stolper, J. T. Stasko, J. Wood, and S. Carpendale, "State of the Art of Sports Data Visualization," Comput. Graph. Forum, vol. 37, no. 3, pp. 663–686, 2018.
  • A. Rusu, D. Stoica, E. Burns, B. Hample, K. McGarry, and R. Russell, "Dynamic visualizations for soccer statistical analysis," Proc. Int. Conf. Inf. Vis., pp. 207–212, 2010.
  • D. Sumpter, Soccermatics: Mathematical Adventures in the Beautiful Game. Bloomsbury Publishing Plc, 2016.
  • L. Gyarmati, H. Kwak, and P. Rodriguez, "Searching for a Unique Style in Soccer," 2014, pp. 5–8.
  • L. Y. Wu, A. J. Danielson, X. J. Hu, and T. B. Swartz, "A contextual analysis of crossing the ball in soccer," J. Quant. Anal. Sport., vol. 17, no. 1, pp. 57–66, 2021.
  • V. Khaustov and M. Mozgovoy, "Recognizing events in spatiotemporal soccer data," Appl. Sci., vol. 10, no. 22, pp. 1–12, 2020.
  • E. Özdemir and H. Alemdar, "Predicting soccer events from optical tracking data," 26th IEEE Signal Process. Commun. Appl. Conf. SIU 2018, pp. 1–4, 2018.
  • P. R. Kamble, A. G. Keskar, and K. M. Bhurchandi, "Ball tracking in sports: a survey," Artif. Intell. Rev., vol. 52, no. 3, pp. 1655–1705, 2019.
  • A. E. Abulwafa, A. I. Saleh, H. A. Ali, and M. S. Saraya, "A fog based ball tracking (FB2T) system using intelligent ball bees," J. Ambient Intell. Humaniz. Comput., vol. 11, no. 11, pp. 5735–5754, 2020.
  • D. G. Cardenas and M. D. Zuniga, "Bullet-Proof Robust Real-Time Ball Tracking," in 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2016, pp. 1–8.
  • A. Lhoest, "Deep Learning for Ball Tracking in Football Sequences," University of Liège, 2020.
  • H. D. Najeeb and R. F. Ghani, "Tracking Ball in Soccer Game Video using Extended Kalman Filter," Proc. 2020 Int. Conf. Comput. Sci. Softw. Eng. CSASE 2020, pp. 78–82, 2020.
  • W. C. Naidoo and J. R. Tapamo, "Soccer video analysis by ball, player and referee tracking," ACM Int. Conf. Proceeding Ser., vol. 204, pp. 51–60, 2006.
  • J. Ren, J. Orwell, G. A. Jones, and M. Xu, "Tracking the soccer ball using multiple fixed cameras," Comput. Vis. Image Underst., vol. 113, no. 5, pp. 633–642, 2009.
  • J. Komorowski, G. Kurzejamski, and G. Sarwas, “BallTrack: Football ball tracking for real-time CCTV systems,” Proc. 16th Int. Conf. Mach. Vis. Appl. MVA 2019, 2019.
  • P. R. Kamble, A. G. Keskar, and K. M. Bhurchandi, "A deep learning ball tracking system in soccer videos," Opto-electronics Rev., vol. 27, no. 1, pp. 58–69, 2019.
  • M. Durus, "Ball Tracking and Action Recognition of Soccer Players in TV Broadcast Videos," Technische Universität München, 2014.
  • J. Komorowski, G. Kurzejamski, and G. Sarwas, "Footandball: Integrated player and ball detector," VISIGRAPP 2020 - Proc. 15th Int. Jt. Conf. Comput. Vision, Imaging Comput. Graph. Theory Appl., vol. 5, pp. 47–56, 2020.
  • M. Leo, P. L. Mazzeo, M. Nitti, and P. Spagnolo, "Accurate ball detection in soccer images using probabilistic analysis of salient regions," Mach. Vis. Appl., vol. 24, no. 8, pp. 1561–1574, 2013.
  • J. Hossein-Khani, H. Soltanian-Zadeh, M. Kamarei, and O. Staadt, "Ball detection with the aim of corner event detection in soccer video," Proc. - 9th IEEE Int. Symp. Parallel Distrib. Process. with Appl. Work. ISPAW 2011 - ICASE 2011, SGH 2011, GSDP 2011, pp. 147–152, 2011.
  • Z. Niu, X. Gao, and Q. Tian, "Tactic analysis based on real-world ball trajectory in soccer video," Pattern Recognit., vol. 45, no. 5, pp. 1937–1947, 2012.
  • S. Baysal and P. Duygulu, "Sentioscope: A Soccer Player Tracking System Using Model Field Particles," IEEE Trans. Circuits Syst. Video Technol., vol. 26, no. 7, pp. 1350–1362, 2016.
  • E. Külah and H. Alemdar, "Quantifying the value of sprints in elite football using spatial cohesive networks," Chaos, Solitons and Fractals, vol. 139, 2020.
  • M. Daszykowski and B. Walczak, “Density-Based Clustering Methods,” Compr. Chemom., vol. 2, pp. 635–654, 2009.
  • N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting," J. Mach. Learn. Res., vol. 15, no. 56, pp. 1929–1958, 2014.
  • L. Prechelt, "Early stopping - But when?," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7700 LECTU, pp. 53–67, 2012.
  • X. Glorot, A. Bordes, and Y. Bengio, "Deep Sparse Rectifier Neural Networks," in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011, vol. 15, pp. 315–323.
  • D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," in 3rd International Conference on Learning Representations, {ICLR} 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015.
  • I. J. Goodfellow et al., "Generative Adversarial Nets," in Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, 2014, pp. 2672–2680.
  • P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, "Image-to-image translation with conditional adversarial networks," Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 5967–5976, 2017.

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

Year 2022, Volume 10, Issue 1, 1 - 8, 01.01.2022
https://doi.org/10.21541/apjess.1060725

Abstract

Tracking the ball location is essential for automated game analysis in complex ball-centered team sports such as football. However, it has always been a challenge for image processing-based techniques because the players and other factors often occlude the view of the ball. This study proposes an automated machine learning-based method for predicting the ball location from players' behavior on the pitch. The model has been built by processing spatial information of players acquired from optical tracking data. Optical tracking data include samples from 300 matches of the 2017-2018 season of the Turkish Football Federation's Super League. We use neural networks to predict the ball location in 2D axes. The average coefficient of determination of the ball tracking model on the test set both for the x-axis and the y-axis is accordingly 79% and 92%, where the mean absolute error is 7.56 meters for the x-axis and 5.01 meters for the y-axis.

References

  • A. Bialkowski, P. Lucey, P. Carr, Y. Yue, S. Sridharan, and I. Matthews, "Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data," Proc. - IEEE Int. Conf. Data Mining, ICDM, vol. 2015-Janua, no. January, pp. 725–730, 2014.
  • B. Skinner and S. J. Guy, "A method for using player tracking data in basketball to learn player skills and predict team performance," PLoS One, vol. 10, no. 9, pp. 1–15, 2015.
  • P. Lucey, D. Oliver, P. Carr, J. Roth, and I. Matthews, "Assessing team strategy using spatiotemporal data," Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. Part F1288, pp. 1366–1374, 2013.
  • C. Perin, R. Vuillemot, C. D. Stolper, J. T. Stasko, J. Wood, and S. Carpendale, "State of the Art of Sports Data Visualization," Comput. Graph. Forum, vol. 37, no. 3, pp. 663–686, 2018.
  • A. Rusu, D. Stoica, E. Burns, B. Hample, K. McGarry, and R. Russell, "Dynamic visualizations for soccer statistical analysis," Proc. Int. Conf. Inf. Vis., pp. 207–212, 2010.
  • D. Sumpter, Soccermatics: Mathematical Adventures in the Beautiful Game. Bloomsbury Publishing Plc, 2016.
  • L. Gyarmati, H. Kwak, and P. Rodriguez, "Searching for a Unique Style in Soccer," 2014, pp. 5–8.
  • L. Y. Wu, A. J. Danielson, X. J. Hu, and T. B. Swartz, "A contextual analysis of crossing the ball in soccer," J. Quant. Anal. Sport., vol. 17, no. 1, pp. 57–66, 2021.
  • V. Khaustov and M. Mozgovoy, "Recognizing events in spatiotemporal soccer data," Appl. Sci., vol. 10, no. 22, pp. 1–12, 2020.
  • E. Özdemir and H. Alemdar, "Predicting soccer events from optical tracking data," 26th IEEE Signal Process. Commun. Appl. Conf. SIU 2018, pp. 1–4, 2018.
  • P. R. Kamble, A. G. Keskar, and K. M. Bhurchandi, "Ball tracking in sports: a survey," Artif. Intell. Rev., vol. 52, no. 3, pp. 1655–1705, 2019.
  • A. E. Abulwafa, A. I. Saleh, H. A. Ali, and M. S. Saraya, "A fog based ball tracking (FB2T) system using intelligent ball bees," J. Ambient Intell. Humaniz. Comput., vol. 11, no. 11, pp. 5735–5754, 2020.
  • D. G. Cardenas and M. D. Zuniga, "Bullet-Proof Robust Real-Time Ball Tracking," in 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2016, pp. 1–8.
  • A. Lhoest, "Deep Learning for Ball Tracking in Football Sequences," University of Liège, 2020.
  • H. D. Najeeb and R. F. Ghani, "Tracking Ball in Soccer Game Video using Extended Kalman Filter," Proc. 2020 Int. Conf. Comput. Sci. Softw. Eng. CSASE 2020, pp. 78–82, 2020.
  • W. C. Naidoo and J. R. Tapamo, "Soccer video analysis by ball, player and referee tracking," ACM Int. Conf. Proceeding Ser., vol. 204, pp. 51–60, 2006.
  • J. Ren, J. Orwell, G. A. Jones, and M. Xu, "Tracking the soccer ball using multiple fixed cameras," Comput. Vis. Image Underst., vol. 113, no. 5, pp. 633–642, 2009.
  • J. Komorowski, G. Kurzejamski, and G. Sarwas, “BallTrack: Football ball tracking for real-time CCTV systems,” Proc. 16th Int. Conf. Mach. Vis. Appl. MVA 2019, 2019.
  • P. R. Kamble, A. G. Keskar, and K. M. Bhurchandi, "A deep learning ball tracking system in soccer videos," Opto-electronics Rev., vol. 27, no. 1, pp. 58–69, 2019.
  • M. Durus, "Ball Tracking and Action Recognition of Soccer Players in TV Broadcast Videos," Technische Universität München, 2014.
  • J. Komorowski, G. Kurzejamski, and G. Sarwas, "Footandball: Integrated player and ball detector," VISIGRAPP 2020 - Proc. 15th Int. Jt. Conf. Comput. Vision, Imaging Comput. Graph. Theory Appl., vol. 5, pp. 47–56, 2020.
  • M. Leo, P. L. Mazzeo, M. Nitti, and P. Spagnolo, "Accurate ball detection in soccer images using probabilistic analysis of salient regions," Mach. Vis. Appl., vol. 24, no. 8, pp. 1561–1574, 2013.
  • J. Hossein-Khani, H. Soltanian-Zadeh, M. Kamarei, and O. Staadt, "Ball detection with the aim of corner event detection in soccer video," Proc. - 9th IEEE Int. Symp. Parallel Distrib. Process. with Appl. Work. ISPAW 2011 - ICASE 2011, SGH 2011, GSDP 2011, pp. 147–152, 2011.
  • Z. Niu, X. Gao, and Q. Tian, "Tactic analysis based on real-world ball trajectory in soccer video," Pattern Recognit., vol. 45, no. 5, pp. 1937–1947, 2012.
  • S. Baysal and P. Duygulu, "Sentioscope: A Soccer Player Tracking System Using Model Field Particles," IEEE Trans. Circuits Syst. Video Technol., vol. 26, no. 7, pp. 1350–1362, 2016.
  • E. Külah and H. Alemdar, "Quantifying the value of sprints in elite football using spatial cohesive networks," Chaos, Solitons and Fractals, vol. 139, 2020.
  • M. Daszykowski and B. Walczak, “Density-Based Clustering Methods,” Compr. Chemom., vol. 2, pp. 635–654, 2009.
  • N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting," J. Mach. Learn. Res., vol. 15, no. 56, pp. 1929–1958, 2014.
  • L. Prechelt, "Early stopping - But when?," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7700 LECTU, pp. 53–67, 2012.
  • X. Glorot, A. Bordes, and Y. Bengio, "Deep Sparse Rectifier Neural Networks," in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011, vol. 15, pp. 315–323.
  • D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," in 3rd International Conference on Learning Representations, {ICLR} 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015.
  • I. J. Goodfellow et al., "Generative Adversarial Nets," in Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, 2014, pp. 2672–2680.
  • P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, "Image-to-image translation with conditional adversarial networks," Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 5967–5976, 2017.

Details

Primary Language English
Subjects Computer Science, Artifical Intelligence
Published Date January 2022
Journal Section Research Articles
Authors

Anar AMİRLİ
Department of Computer Science, Saarland University, Saarland, Germany
0000-0002-7094-3114
Germany


Hande ALEMDAR This is me (Primary Author)
Department of Computer Engineering, Middle East Technical University, Ankara
0000-0002-1397-2146
Türkiye

Early Pub Date January 20, 2022
Publication Date January 1, 2022
Application Date June 15, 2021
Acceptance Date September 27, 2021
Published in Issue Year 2022, Volume 10, Issue 1

Cite

IEEE A. Amirli and H. Alemdar , "Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data", Academic Platform Journal of Engineering and Smart Systems, vol. 10, no. 1, pp. 1-8, Jan. 2022, doi:10.21541/apjess.1060725

Academic Platform Journal of Engineering and Smart Systems