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Football Players Movement Analysis in Panning Videos

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12746))

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Abstract

In this paper, we present an end-to-end application to perform automatic multiple player detection, unsupervised labelling, and a semi-automatic approach to finding homographies. We incorporate dense optical flow for modelling camera movement and user-assisted calibration on automatically chosen key-frames. Players detection is performed with a pre-trained YOLOv3 detector and player labelling is done using features in HSV colorspace. The experimental results demonstrate that our method is reliable with generating heatmaps from players’ positions in case of moderate camera movement. Major limitations of proposed method are the necessity of manual calibration of characteristic frames, inaccuracy with fast camera movements, and small tolerance of vertical camera movement.

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References

  1. Baysal, S., Duygulu, P.: Sentioscope: a soccer player tracking system using model field particles. IEEE Trans. Circuits Syst. Video Technol. 26(7), 1350–1362 (2015)

    Article  Google Scholar 

  2. Beauchemin, S.S., Barron, J.L.: The computation of optical flow. ACM Comput. Surv. (CSUR) 27(3), 433–466 (1995)

    Article  Google Scholar 

  3. Chen, J., Little, J.J.: Sports camera calibration via synthetic data. CoRR abs/1810.10658 (2018). http://arxiv.org/abs/1810.10658

  4. Chen, J., Little, J.J.: Sports camera calibration via synthetic data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  5. Chen, J., Zhu, F., Little, J.J.: A two-point method for PTZ camera calibration in sports. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 287–295. IEEE (2018)

    Google Scholar 

  6. Chiang, T.K., Leou, J.J., Lin, C.S.: An improved mean shift algorithm based tracking system for soccer game analysis. In: Proceedings: APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference, pp. 380–385 (2009)

    Google Scholar 

  7. Cyganek, B., Woźniak, M.: Tensor-based shot boundary detection in video streams. New Gener. Comput. 35(4), 311–340 (2017). https://doi.org/10.1007/s00354-017-0024-0

  8. Dalka, P.: Methods of algorithmic analysis of the video image for applications in traffic monitoring [in Polish: Metody algorytmicznej analizy obrazu wizyjnego do zastosowań w monitorowaniu ruchu drogowego]. Ph.D. thesis, Gdansk University of Technology (2015)

    Google Scholar 

  9. D’Orazio, T., Leo, M.: A review of vision-based systems for soccer video analysis. Pattern Recogn. 43(8), 2911–2926 (2010)

    Article  Google Scholar 

  10. Enomoto, A., Saito, H.: AR display for observing sports events based on camera tracking using pattern of ground. In: Shumaker, R. (ed.) VMR 2009. LNCS, vol. 5622, pp. 421–430. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02771-0_47

    Chapter  Google Scholar 

  11. Frejlichowski, D.: A method for data extraction from video sequences for automatic identification of football players based on their numbers. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011. LNCS, vol. 6978, pp. 356–364. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24085-0_37

    Chapter  Google Scholar 

  12. Ghanem, B., Zhang, T., Ahuja, N.: Robust video registration applied to field-sports video analysis. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 2. Citeseer (2012)

    Google Scholar 

  13. Homayounfar, N., Fidler, S., Urtasun, R.: Sports field localization via deep structured models. In: Procedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5212–5220 (2017)

    Google Scholar 

  14. Wang, J., Chng, E., Xu, C.: Soccer replay detection using scene transition structure analysis. In: Proceedings (ICASSP 2005). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, vol. 2, pp. ii/433–ii/436 (2005). https://doi.org/10.1109/ICASSP.2005.1415434

  15. Johnson, N.: Extracting player tracking data from video using non-stationary cameras and a combination of computer vision techniques. In: Proceedings of the 14th MIT Sloan Sports Analytics Conference, Boston, MA, USA (2020)

    Google Scholar 

  16. Larson, N.G., Stevens, K.A.: Automated camera-based tracking system for sports contests, US Patent 5,363,297, 8 November 1994

    Google Scholar 

  17. Leveaux, R., Messerschmitt, M.: The changing shape of sport through information technologies. In: Proceedings of the 26th International Business Information Management Association Conference-Innovation Management and Sustainable Economic Competitive Advantage: From Regional Development to Global Growth, IBIMA 2015 (2015)

    Google Scholar 

  18. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  19. Mackowiak, S., Konieczny, J.: Player extraction in sports video sequences. In: 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 409–412. IEEE (2012)

    Google Scholar 

  20. Manafifard, M., Ebadi, H., Moghaddam, H.A.: A survey on player tracking in soccer videos. Comput. Vis. Image Underst. 159, 19–46 (2017)

    Article  Google Scholar 

  21. Najafzadeh, N., Fotouhi, M., Kasaei, S.: Multiple soccer players tracking. In: 2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 310–315. IEEE (2015)

    Google Scholar 

  22. Nowosielski, A., Frejlichowski, D., Forczmanski, P., Gosciewska, K., Hofman, R.: Automatic analysis of vehicle trajectory applied to visual surveillance. In: Choras, RS (ed.) Image Processing And Communications Challenges 7. Advances in Intelligent Systems and Computing, vol. 389, pp. 89–96 (2016). https://doi.org/10.1007/978-3-319-23814-2_11

  23. Ponnusamy, A.: cvlib - high level computer vision library for Python (2018). https://github.com/arunponnusamy/cvlib

  24. Puwein, J., Ziegler, R., Vogel, J., Pollefeys, M.: Robust multi-view camera calibration for wide-baseline camera networks. In: 2011 IEEE Workshop on Applications of Computer Vision (WACV), pp. 321–328. IEEE (2011)

    Google Scholar 

  25. Ramanathan, V., Huang, J., Abu-El-Haija, S., Gorban, A., Murphy, K., Fei-Fei, L.: Detecting events and key actors in multi-person videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3043–3053 (2016)

    Google Scholar 

  26. Redmon, J., Farhadi, A.: YOLOV3: an incremental improvement (2018). arXiv preprint arXiv:1804.02767

  27. Santhosh, P., Kaarthick, B.: An automated player detection and tracking in basketball game. Comput. Mater. Continua 58(3), 625–639 (2019)

    Article  Google Scholar 

  28. Sharma, R.A., Bhat, B., Gandhi, V., Jawahar, C.: Automated top view registration of broadcast football videos. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 305–313. IEEE (2018)

    Google Scholar 

  29. Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, 2003. vol. 2, pp. 1398–1402 (2003). https://doi.org/10.1109/ACSSC.2003.1292216

  30. Yi, Z., Yongliang, S., Jun, Z.: An improved tiny-yolov3 pedestrian detection algorithm. Optik 183, 17–23 (2019)

    Article  Google Scholar 

  31. Zhu, G., et al.: Trajectory based event tactics analysis in broadcast sports video. In: Proceedings of the 15th ACM International Conference on Multimedia, pp. 58–67 (2007)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Żołnierska Street 49, 71-210, Szczecin, Poland

    Karol Działowski & Paweł Forczmański

Authors
  1. Karol Działowski
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  2. Paweł Forczmański
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Corresponding author

Correspondence to Paweł Forczmański .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Działowski, K., Forczmański, P. (2021). Football Players Movement Analysis in Panning Videos. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12746. Springer, Cham. https://doi.org/10.1007/978-3-030-77977-1_15

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  • DOI: https://doi.org/10.1007/978-3-030-77977-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77976-4

  • Online ISBN: 978-3-030-77977-1

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