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Annals of Computer Science and Information Systems, Volume 17

Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems

Soccer object motion recognition based on 3D convolutional neural networks

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DOI: http://dx.doi.org/10.15439/2018F48

Citation: Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 17, pages 129134 ()

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Abstract. Due to the development of video understanding and big data analysis research field using deep learning technique, intelligent machines have replaced the tasks that people performed in the past in various fields such as traffic, surveillance, and security area. In the sports field, especially in soccer games, it is also attempting quantitative analysis of players and games through deep learning or big data analysis technique. However, because of the nature of soccer analysis, it is still difficult to make sophisticated automatic analysis due to technical limitations. In this paper, we propose a deep learning based motion recognition technique which is the basis of high level automatic soccer analysis. For sophisticated motion recognition, we maximize recognition accuracy by sequentially processing the data in three steps: data acquisition, data augmentation, and 3D CNN based motion classifier learning. As can be seen from the experimental results, the proposed method guarantees real-time speed performance and satisfactory accuracy performance.


  1. DartFish sports analysis tool [Online] Available: http://www.dartfish.com
  2. Hawk-eye innovations [Online] Available: https://www.hawkeyeinno.vations.com
  3. FreeD on NFL [Online] Available : https://newsroom.intel.com/news/ intel-nfl-kickoff-freed-technology-11-stadiums-create-immersive-highlights-2017-season/
  4. “Sports analytics: market shares, strategies, and forecasts, worldwide, 2015 to 2021,” Wintergreen Research, 472 pages, May 2015
  5. A. Ghosh, “How `Match Insight' is changing soccer,” 6th Aug. 2014. [Online] Available: https://blogs.sap.com/2014/08/06/how-software-is-making-football-even-more-beautiful/
  6. C. P. Huang, C. H. Hsieh, K. T. Lai, and W. Y. Huang, “Human action recognition using histogram of oriented gradient of motion history image,” in International Conference on Instrumentation, Measurement, Computer, Communication and Control, pp. 353-356, Oct. 2011.
  7. L. Hu, W. Liu, B. Li, and W. Xing, “Robust motion detection using histogram of oriented gradients for illumination variations,” in Proc. ICIMA 2010, pp. 443-447, May. 2010.
  8. P. Banerjee and S. Sengupta, “Human motion detection and tracking for video surveillance,” in National Conference for Communication, 2008.
  9. O. Patsadu, C. Nukoolkit, and B. Watanapa, “Human gesture recognition using Kinect camera,” in Proc. JCSSE 2012, pp. 28-32, May. 2012.
  10. E. E. Stone and M. Skubic, “Fall detection in homes of older adults using the Microsoft Kinect,” IEEE Jour. Biomedical and Health Informatics, vol. 19, no. 1, pp. 290-301, Mar. 2014.
  11. N. C. Kiliboz and U. Gudukbay, “A hand gesture recognition for human computer interaction,” Jour. Visual Communication and Image Representation, vol. 28, pp. 97-104, Apr. 2015.
  12. M. B. Brahem, B. J. Menelas, and M. D. Otis, “Use of 3DOF accelerometer for foot tracking and gesture recognition in mobile HCI,” Peocedia Computer Science, vol. 19, pp. 453-460, 2013.
  13. Y. LeCun, Y. Bengio, and G. E. Hinton, “Deep learning,” in Nature, vol. 521, pp. 436-444, May. 2015.
  14. J. Lee, Y. Kim, M. Jeong, C. Kim, D. Nam, J. Lee, S. Moon, and W. Yoo, “3D convolutional neural networks for soccer object motion recognition,” in Proc. ICACT 2018, pp. 354-358, Feb. 2018.
  15. W. Kim, S. Moon, J. Lee, D. Nam, and C. Jung , “Multiple Player Tracking in Soccer Videos : An Adaptive Multiscale Sampling Approach,” Multimedia Systems, pp. 1-13, Feb. 2018.
  16. A. Krizhevsky, I. Sutskever, and G. E. hinton, “ImageNet classification with deep convolutional neural network,” in Proc. NIPS 2012, pp. 1-9, Dec. 2012.
  17. S. Ji, W. Xu, M. Yang, and K. Yu, “3D convolutional neural networks for human action recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 221-231, Mar. 2012.
  18. D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proc. ICCV 2015, pp. 4489-4497, Dec. 2015.
  19. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng, “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv prepreprint https://arxiv.org/abs/1603.04467v2, 2016.