Skip to main content
Springer Nature Link
Log in

A survey on soccer player detection and tracking with videos

  • Review
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Soccer is a popular sport, and there is a growing need for automated analysis of soccer videos, while the detection and tracking of the players is the indispensable prerequisite. In this paper, we first introduce and classify multi-object tracking and then present two mostly used multi-object tracking methods, DeepSort and TrackFormer. When multi-object tracking is applied to soccer scenarios, some preprocessing and post-processing are generally performed, with preprocessing including processing of the video, such as splicing and background removing, and post-processing including further applications, such as player mapping for a 2D stadium. By directly employing the two methods above, we test the real scene and train TrackFormer to get further results. Meanwhile, in order to facilitate researchers who are interested in multi-object tracking as well as in the direction of player tracking, recent advances in preprocessing and processing methods for soccer player tracking are given and future research directions are suggested.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

No datasets were generated or analyzed during the current study.

References

  1. Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 8844–8854 (2022)

  2. Chu, P., Wang, J., You, Q., Ling, H., Liu, Z.: Transmot: spatial-temporal graph transformer for multiple object tracking. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 4870–4880 (2023)

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

    Article  MATH  Google Scholar 

  4. Najeeb, H.D., Ghani, R.F.: A survey on object detection and tracking in soccer videos. MJPS 8(1), 1–13 (2021)

    Article  MATH  Google Scholar 

  5. Fashing, M., Tomasi, C.: Mean shift is a bound optimization. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 471–474 (2005)

    Article  MATH  Google Scholar 

  6. Carreira-Perpinán, M.A.: A review of mean-shift algorithms for clustering. arXiv preprint arXiv:1503.00687 (2015)

  7. Comaniciu, D., Meer, P.: Mean shift analysis and applications. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1197–1203. IEEE (1999)

  8. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)

    Article  MATH  Google Scholar 

  9. Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)

    Article  MATH  Google Scholar 

  10. Wang, H., Hu, D.: Comparison of SVM and LS-SVM for regression. In: 2005 International Conference on Neural Networks and Brain, vol. 1, pp. 279–283. IEEE (2005)

  11. Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 3, pp. 32–36. IEEE (2004)

  12. Vishwanathan, S.V.M., Murty, M.N.: SSVM: a simple svm algorithm. In: Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No. 02CH37290), vol. 3, pp. 2393–2398. IEEE (2002)

  13. Cherkassky, V., Ma, Y.: Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw. 17(1), 113–126 (2004)

    Article  MATH  Google Scholar 

  14. Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In Computer Vision-ECCV’96: 4th European Conference on Computer Vision Cambridge, 1996 Proceedings, Vol. I 4, pp. 343–356. Springer (1996)

  15. Li, P., Zhang, T., Pece, A.E.C.: Visual contour tracking based on particle filters. Image Vis. Comput. 21(1), 111–123 (2003)

    Article  MATH  Google Scholar 

  16. Li, M., Kambhamettu, C., Stone, M.: Automatic contour tracking in ultrasound images. Clin. Linguist. Phon. 19(6–7), 545–554 (2005)

    Article  MATH  Google Scholar 

  17. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)

    Article  MATH  Google Scholar 

  18. Mori, G., Belongie, S., Malik, J.: Efficient shape matching using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1832–1837 (2005)

    Article  MATH  Google Scholar 

  19. Veltkamp, R.C.: Shape matching: similarity measures and algorithms. In: Proceedings International Conference on Shape Modeling and Applications, pp. 188–197. IEEE (2001)

  20. Bar-Shalom, Y., Fortmann, T.E., Cable, P.G.: Tracking and data association (1990)

  21. Streit, R.L., Luginbuhl, T.E.: Maximum likelihood method for probabilistic multihypothesis tracking. In: Signal and Data Processing of Small Targets 1994, vol. 2235, pp. 394–405. SPIE (1994)

  22. Shitrit, H.B., Berclaz, J., Fleuret, F., Fua, P.: Multi-commodity network flow for tracking multiple people. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1614–1627 (2013)

    Article  MATH  Google Scholar 

  23. Liu, J., Carr, P., Collins, R.T., Liu, Y.: Tracking sports players with context-conditioned motion models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1830–1837 (2013)

  24. Aharon, N., Orfaig, R., Bobrovsky, B.-Z.: Bot-sort: Robust associations multi-pedestrian tracking. arXiv preprint arXiv:2206.14651 (2022)

  25. Maggiolino, G., Ahmad, A., Cao, J., Kitani, K.: Deep OC-sort: multi-pedestrian tracking by adaptive re-identification. arXiv preprint arXiv:2302.11813 (2023)

  26. Pang, J., Qiu, L., Li, X., Chen, H., Li, Q., Darrell, T., Yu, F.: Quasi-dense similarity learning for multiple object tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 164–173 (2021)

  27. Zhang, Y., Wang, C., Wang, X., Zeng, W., Liu, W.: Fairmot: On the fairness of detection and re-identification in multiple object tracking. Int. J. Comput. Vis. 129, 3069–3087 (2021)

    Article  MATH  Google Scholar 

  28. Liu, S., Li, X., Lu, H., He, Y.: Multi-object tracking meets moving UAV. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8876–8885 (2022)

  29. Liu, Q., Chu, Q., Liu, B., Yu, N.: GSM: Graph similarity model for multi-object tracking. In: IJCAI, pp. 530–536 (2020)

  30. Hyun, J., Kang, M., Wee, D., Yeung, D.-Y.: Detection recovery in online multi-object tracking with sparse graph tracker. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 4850–4859 (2023)

  31. Wang, Q., Zheng, Y., Pan, P., Xu, Y.: Multiple object tracking with correlation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3876–3886 (2021)

  32. Cai, J., Xu, M., Li, W., Xiong, Y., Xia, W., Tu, Z., Soatto, S.: Memot: multi-object tracking with memory. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8090–8100 (2022)

  33. Wang, Y., Weng, X., Kitani, K.: Joint detection and multi-object tracking with graph neural networks. arXiv preprint arXiv:2006.13164, 1(2) (2020)

  34. Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., Wang, X.: Bytetrack: multi-object tracking by associating every detection box. In: European Conference on Computer Vision, pp. 1–21. Springer (2022)

  35. Liu, Z., Wang, X., Wang, C., Liu, W., Bai, X.: Sparsetrack: multi-object tracking by performing scene decomposition based on pseudo-depth. arXiv preprint arXiv:2306.05238 (2023)

  36. Yunhao, D., Zhao, Z., Song, Y., Zhao, Y., Fei, S., Gong, T., Meng, H.: Make deepsort great again. IEEE Trans. Multimed. 5, 55 (2023). https://doi.org/10.1109/TMM.2023.3240881

    Article  Google Scholar 

  37. Girbau, A., Marqués, F., Satoh, S.: Multiple object tracking from appearance by hierarchically clustering tracklets. arXiv preprint arXiv:2210.03355 (2022)

  38. Li, J., Ding, Y., Wei, H.-L., Zhang, Y., Lin, W.: Simpletrack: Rethinking and improving the jde approach for multi-object tracking. Sensors 22, 5863 (2022)

    Article  MATH  Google Scholar 

  39. Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645–3649. IEEE (2017)

  40. Welch, G.F.: Kalman filter. Computer vision: a reference guide, pp. 1–3 (2020)

  41. Meinhold, R.J., Singpurwalla, N.D.: Understanding the Kalman filter. Am. Stat. 37(2), 123–127 (1983)

    Article  MATH  Google Scholar 

  42. Li, Q., Li, R., Ji, K., Dai, W.: Kalman filter and its application. In: 2015 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), pp. 74–77. IEEE (2015)

  43. Bishop, G., Welch, G., et al.: An introduction to the Kalman filter. In: Proc of SIGGRAPH, Course 8(27599–23175), 41 (2001)

  44. Luiten, J., Osep, A., Dendorfer, P., Torr, P., Geiger, A., Leal-Taixé, L., Leibe, B.: Hota: a higher order metric for evaluating multi-object tracking. Int. J. Comput. Vis. 129, 548–578 (2021)

    Article  Google Scholar 

  45. He, X.: Application of deep learning in video target tracking of soccer players. Soft. Comput. 26(20), 10971–10979 (2022)

    Article  MATH  Google Scholar 

  46. Lee, J., Moon, S., Nam, D.-W., Lee, J., Oh, A.R., Yoo, W.: A study on sports player tracking based on video using deep learning. In: 2020 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1161–1163. IEEE (2020)

  47. Cuevas, C., Quilón, D., García, N.: Techniques and applications for soccer video analysis: a survey. Multimed. Tools Appl. 79(39–40), 29685–29721 (2020)

    Article  Google Scholar 

  48. Cioppa, A., Giancola, S., Deliege, A., Kang, L., Zhou, X., Cheng, Z., Ghanem, B., Van Droogenbroeck, M.: Soccernet-tracking: Multiple object tracking dataset and benchmark in soccer videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3491–3502 (2022)

  49. Yu, J., Lei, A., Song, Z., Wang, T., Cai, H., Feng, N.: Comprehensive dataset of broadcast soccer videos. In: 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 418–423. IEEE (2018)

  50. Feng, N., Song, Z., Yu, J., Chen, Y.-P.P., Zhao, Y., He, Y., Guan, T.: SSET: a dataset for shot segmentation, event detection, player tracking in soccer videos. Multimed. Tools Appl. 79, 28971–28992 (2020)

    Article  Google Scholar 

  51. Vandeghen, R., Cioppa, A., Van Droogenbroeck, M.: Semi-supervised training to improve player and ball detection in soccer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3481–3490 (2022)

  52. Komorowski, J., Kurzejamski, G., Sarwas, G.: Footandball: integrated player and ball detector. arXiv preprint arXiv:1912.05445 (2019)

  53. Lu, K., Chen, J., Little, J.J., He, H.: Light cascaded convolutional neural networks for accurate player detection. arXiv preprint arXiv:1709.10230 (2017)

  54. Wei-Lwun, L., Ting, J.-A., Little, J.J., Murphy, K.P.: Learning to track and identify players from broadcast sports videos. IEEE Trans. Pattern Anal. Mach. Intell. 35(7), 1704–1716 (2013)

    Article  Google Scholar 

  55. Xing, J., Ai, H., Liu, L., Lao, S.: Multiple player tracking in sports video: a dual-mode two-way Bayesian inference approach with progressive observation modeling. IEEE Trans. Image Process. 20(6), 1652–1667 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  56. Kristan, M., Perš, J., Perše, M., Kovačič, S.: Closed-world tracking of multiple interacting targets for indoor-sports applications. Comput. Vis. Image Underst. 113(5), 598–611 (2009)

    Article  MATH  Google Scholar 

  57. Misu, T., Naemura, M., Zheng, W., Izumi, Y., Fukui, K.: Robust tracking of soccer players based on data fusion. In: 2002 International Conference on Pattern Recognition, vol. 1, pp. 556–561. IEEE (2002)

  58. Martín, R., Martínez, J.M.: A semi-supervised system for players detection and tracking in multi-camera soccer videos. Multimed. Tools Appl. 73, 1617–1642 (2014)

    Article  MATH  Google Scholar 

  59. Morais, E., Goldenstein, S., Ferreira, A., Rocha, A.: Automatic tracking of indoor soccer players using videos from multiple cameras. In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 174–181. IEEE (2012)

  60. Morais, E., Ferreira, A., Cunha, S.A., Barros, R.M.L., Rocha, A., Goldenstein, S.: A multiple camera methodology for automatic localization and tracking of futsal players. Pattern Recognit. Lett. 39, 21–30 (2014)

    Article  Google Scholar 

  61. Shitrit, H.B., Berclaz, J., Fleuret, F., Fua, P.: Tracking multiple people under global appearance constraints. In: 2011 International Conference on Computer Vision, pp. 137–144. IEEE (2011)

  62. Bai, X., Zhang, T., Song, X., Niu, X.: Playfield detection using color ratio and local entropy. In: 2011 seventh international conference on intelligent information hiding and multimedia signal processing, pp. 356–359. IEEE (2011)

  63. Sabirin, H., Sankoh, H., Naito, S.: Automatic soccer player tracking in single camera with robust occlusion handling using attribute matching. IEICE Trans. Inf. Syst. 98(8), 1580–1588 (2015)

    Article  Google Scholar 

  64. Cuevas, C., Quilon, D., García, N.: Automatic soccer field of play registration. Pattern Recogn. 103, 107278 (2020)

    Article  Google Scholar 

  65. Chu, Y.-J., Su, J.-W., Hsiao, K.-W., Lien, C.-Y., Fan, S.-H., Hu, M.-C., Lee, R.-R., Yao, C.-Y., Chu, H.-K.: Sports field registration via keypoints-aware label condition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3523–3530 (2022)

  66. Bu, J., Lao, S., Bai, L.: Automatic line mark recognition and its application in camera calibration in soccer video. In: 2011 IEEE International Conference on Multimedia and Expo, pp. 1–6. IEEE (2011)

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

  68. Sha, L., Hobbs, J., Felsen, P., Wei, X., Lucey, P., Ganguly, S.: End-to-end camera calibration for broadcast videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13627–13636 (2020)

  69. Duh, D.-J., Chang, S.-Y., Chen, S.-Y., Kan, C.-C.: Automatic broadcast soccer video analysis, player detection, and tracking based on color histogram. In: Intelligent Technologies and Engineering Systems, pp. 123–130. Springer (2013)

  70. Yang, Y., Li, D.: Robust player detection and tracking in broadcast soccer video based on enhanced particle filter. J. Vis. Commun. Image Represent. 46, 81–94 (2017)

    Article  MATH  Google Scholar 

  71. Sverrisson, S., Grancharov, V., Pobloth, H.: Real-time tracking-by-detection in broadcast sports videos. In: Image Analysis: 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings 21, pp. 399–411. Springer (2019)

  72. Li, H., Flierl, M.: Sift-based multi-view cooperative tracking for soccer video. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1001–1004. IEEE (2012)

  73. Herrmann, M., Hoernig, M., Radig, B.: Online multi-player tracking in monocular soccer videos. Aasri Proced. 8, 30–37 (2014)

    Article  Google Scholar 

  74. 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  MATH  Google Scholar 

  75. 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)

  76. Kim, W., Moon, S.-W., Lee, J., Nam, D.-W., Jung, C.: Multiple player tracking in soccer videos: an adaptive multiscale sampling approach. Multimed. Syst. 24, 611–623 (2018)

    Article  MATH  Google Scholar 

  77. Kim, W.: Multiple object tracking in soccer videos using topographic surface analysis. J. Vis. Commun. Image Represent. 65, 102683 (2019)

    Article  Google Scholar 

  78. Hurault, S., Ballester, C., Haro, G.: Self-supervised small soccer player detection and tracking. In: Proceedings of the 3rd International Workshop on Multimedia Content Analysis in Sports, pp. 9–18 (2020)

  79. Naik, B.T., Hashmi, M.F., Geem, Z.W., Bokde, N.D.: Deepplayer-track: player and referee tracking with jersey color recognition in soccer. IEEE Access 10, 32494–32509 (2022)

    Article  Google Scholar 

  80. Zheng, B.: Soccer player video target tracking based on deep learning. Mob. Inf. Syst. 1–6, 2022 (2022)

    MATH  Google Scholar 

  81. Theiner, J., Gritz, W., Müller-Budack, E., Rein, R., Memmert, D., Ewerth, R.: Extraction of positional player data from broadcast soccer videos. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 823–833 (2022)

  82. Scott, A., Uchida, I., Onishi, M., Kameda, Y., Fukui, K., Fujii, K.: Soccertrack: a dataset and tracking algorithm for soccer with fish-eye and drone videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3569–3579 (2022)

  83. Kim, J.-Y., Kim, T.-Y.: Soccer ball tracking using dynamic Kalman filter with velocity control. In: 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization, pp. 367–374. IEEE (2009)

  84. Najeeb, H.D., Ghani, R.F.: Tracking ball in soccer game video using extended Kalman filter. In: 2020 International Conference on Computer Science and Software Engineering (CSASE), pp. 78–82. IEEE (2020)

  85. Kamble, P.R., Keskar, A.G., Bhurchandi, K.M.: A deep learning ball tracking system in soccer videos. Opto-Electron. Rev. 27(1), 58–69 (2019)

    Article  MATH  Google Scholar 

  86. Ben Shitrit, H., Raca, M., Fleuret, F., Fua, P.: Tracking multiple players using a single camera. Technical report, Springer Verlag (2013)

  87. Zhang, R., Lingxiang, W., Yang, Y., Wanneng, W., Chen, Y., Min, X.: Multi-camera multi-player tracking with deep player identification in sports video. Pattern Recogn. 102, 107260 (2020)

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (62077037, 62376231,62365014, U22B2034, 62262043, 62171321, 62162044 and 62162414) and in part by the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No. VRLAB2023B01).

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Beijing Forestry University, Beijing, China

    Chao Yang, Meng Yang, Hongyu Li & Linlu Jiang

  2. Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forest and Grassland Administration, Beijing, China

    Meng Yang

  3. School of Physical Education, Shanghai University of Sport, Shanghai, China

    Xiang Suo, Lijuan Mao & Zhen Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Weiliang Meng

Authors
  1. Chao Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Meng Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Hongyu Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Linlu Jiang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Xiang Suo
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Lijuan Mao
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Weiliang Meng
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Zhen Li
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Chao Yang wrote the main manuscript. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Meng Yang or Zhen Li.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, C., Yang, M., Li, H. et al. A survey on soccer player detection and tracking with videos. Vis Comput 41, 815–829 (2025). https://doi.org/10.1007/s00371-024-03367-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-024-03367-6

Keywords