Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Classifying Soccer Ball-on-Goal Position Through Kicker Shooting Action (2312.15236v1)

Published 23 Dec 2023 in cs.CV

Abstract: This research addresses whether the ball's direction after a soccer free-kick can be accurately predicted solely by observing the shooter's kicking technique. To investigate this, we meticulously curated a dataset of soccer players executing free kicks and conducted manual temporal segmentation to identify the moment of the kick precisely. Our approach involves utilizing neural networks to develop a model that integrates Human Action Recognition (HAR) embeddings with contextual information, predicting the ball-on-goal position (BoGP) based on two temporal states: the kicker's run-up and the instant of the kick. The study encompasses a performance evaluation for eleven distinct HAR backbones, shedding light on their effectiveness in BoGP estimation during free-kick situations. An extra tabular metadata input is introduced, leading to an interesting model enhancement without introducing bias. The promising results reveal 69.1% accuracy when considering two primary BoGP classes: right and left. This underscores the model's proficiency in predicting the ball's destination towards the goal with high accuracy, offering promising implications for understanding free-kick dynamics in soccer.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (37)
  1. Reidentifying soccer players in broadcast videos using body feature alignment based on pose. In Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things, page 440–444, New York, NY, USA. Association for Computing Machinery.
  2. The effects of facial expression and relaxation cues on movement economy, physiological, and perceptual responses during running. Psychology of Sport and Exercise, 34:20–28.
  3. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4724–4733.
  4. A bottom-up approach based on semantics for the interpretation of the main camera stream in soccer games. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 1846–184609.
  5. Soccernet-v2 : A dataset and benchmarks for holistic understanding of broadcast soccer videos. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.
  6. Deloitte (2023). Deloitte football money league 2023. Accessed on November 3, 2023.
  7. Feichtenhofer, C. (2020). X3D: Expanding Architectures for Efficient Video Recognition. 2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), pages 200–210.
  8. Slowfast networks for video recognition. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 6201–6210.
  9. A large-scale study on unsupervised spatiotemporal representation learning. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3298–3308.
  10. FIFA (2022). 2019-2022 revenue. Accessed on November 3, 2023.
  11. Towards cumulative race time regression in sports: I3D ConvNet transfer learning in ultra-distance running events. In International Conference on Pattern Recognition (ICPR), pages 805–811.
  12. A Large-Scale Re-identification Analysis in Sporting Scenarios: the Betrayal of Reaching a Critical Point. In International Joint Conference on Biometrics (IJCB).
  13. Automatic key moment extraction and highlights generation based on comprehensive soccer video understanding. In 2020 IEEE International Conference on Multimedia Expo Workshops (ICMEW), pages 1–6.
  14. Soccernet: A scalable dataset for action spotting in soccer videos. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 1792–1810.
  15. Detection of ice hockey players and teams via a two-phase cascaded cnn model. IEEE Access, 8:195062–195073.
  16. He, X. (2022). Application of deep learning in video target tracking of soccer players. Soft Computing, 26(20):10971–10979.
  17. Sports field localization via deep structured models. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4012–4020.
  18. Clustered pose and nonlinear appearance models for human pose estimation. In Proc. BMVC, pages 12.1–11.
  19. A deep learning ball tracking system in soccer videos. Opto-Electronics Review, 27(1):58–69.
  20. The Kinetics Human Action Video Dataset. CoRR.
  21. A study on sports player tracking based on video using deep learning. In 2020 International Conference on Information and Communication Technology Convergence (ICTC), pages 1161–1163.
  22. Li, L. and Li Fei-Fei (2007). What, where and who? classifying events by scene and object recognition. In 2007 IEEE 11th International Conference on Computer Vision, pages 1–8.
  23. Design and implementation of a soccer ball detection system with multiple cameras. ArXiv, abs/2302.00123.
  24. Microsoft (2023). Shaping the future of the game. Accessed on November 3, 2023.
  25. Action quality assessment across multiple actions. In IEEE Winter Conference on Applications of Computer Vision, WACV 2019, Waikoloa Village, HI, USA, January 7-11, 2019, pages 1468–1476. IEEE.
  26. What and how well you performed? A multitask learning approach to action quality assessment. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pages 304–313. Computer Vision Foundation / IEEE.
  27. TGC20ReId: A dataset for sport event re-identification in the wild. Pattern Recognition Letters, 138:355–361.
  28. Facial expression analysis in a wild sporting environment. Multimedia Tools and Applications, 82(8):11395–11415.
  29. Shih, H.-C. (2018). A survey of content-aware video analysis for sports. IEEE Transactions on Circuits and Systems for Video Technology, 28(5):1212–1231.
  30. Two-stream convolutional networks for action recognition in videos. ArXiv, abs/1406.2199.
  31. Bring it to the pitch: Combining video and movement data to enhance team sport analysis. IEEE Transactions on Visualization and Computer Graphics, 24(1):13–22.
  32. Trajectory prediction with imitation learning reflecting defensive evaluation in team sports. In 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), pages 124–125.
  33. Tracking a golf ball with high-speed stereo vision system. IEEE Transactions on Instrumentation and Measurement, 68(8):2742–2754.
  34. Non-local neural networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7794–7803.
  35. Forvizor: Visualizing spatio-temporal team formations in soccer. IEEE Transactions on Visualization and Computer Graphics, 25(1):65–75.
  36. Learning to score figure skating sport videos. IEEE Transactions on Circuits and Systems for Video Technology, 30(12):4578–4590.
  37. ByteTrack: Multi-Object Tracking by Associating Every Detection Box. In European Conference on Computer Vision.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now