Abstract
Modern technology is strongly associated with sports. A perfect example of machine learning in sports is a support of detection of specific events or situations. Such a problem is present in boxing, where boxers’ moves need to be precisely detected. However video analysis is labor intensive but may provide valuable information. The paper presents the problem of processing recordings of boxing boxers, in which the dynamics is at an extremely high level and some events last for fractions of seconds. Additionally, the competition is often watched by spectators blocking the view. The goal of this paper is to present accurate, precise and quick method of detecting the presence of pugilists in the ring. This will allow to evaluate and score the boxing fight later. To validate the experiment, relevant material had to be collected – the authors recorded real boxing bouts and prepared the complete training set. The proposed solution will be used to automatically filter-out uninteresting parts of footage, where boxers are not engaged in close-combats situation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Braun, M., Krebs, S., Flohr, F., Gavrila, D.M.: Eurocity persons: a novel benchmark for person detection in traffic scenes. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1844–1861 (2019). https://doi.org/10.1109/TPAMI.2019.2897684
Burić, M., Pobar, M., Ivašić-Kos, M.: Object detection in sports videos. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1034–1039. IEEE (2018)
Chen, W., Shi, Y.Q., Xuan, G.: Identifying computer graphics using HSV color model and statistical moments of characteristic functions. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 1123–1126. IEEE (2007)
Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, pp. 379–387 (2016)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Hahn, A., et al.: Development of an automated scoring system for amateur boxing. Procedia Eng. 2(2), 3095–3101 (2010)
Jeffries, C.T.: Sports analytics with computer vision. The College of Wooster (2018)
Kolkur, S., Kalbande, D., Shimpi, P., Bapat, C., Jatakia, J.: Human skin detection using RGB, HSV and YCBCR color models. arXiv preprint arXiv:1708.02694 (2017)
Kundid Vasić, M., Papić, V.: Multimodel deep learning for person detection in aerial images. Electronics 9(9), 1459 (2020)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)
Setterwall, D.: Computerised video analysis of football-technical and commercial possibilities for football coaching. Unpublished Masters Thesis, Stockholms Universitet (2003)
Stein, M., et al.: Bring it to the pitch: combining video and movement data to enhance team sport analysis. IEEE Trans. Visual Comput. Graphics 24(1), 13–22 (2017)
Sudhir, G., Lee, J.C.M., Jain, A.K.: Automatic classification of tennis video for high-level content-based retrieval. In: Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database, pp. 81–90. IEEE (1998)
Thomas, G., Gade, R., Moeslund, T.B., Carr, P., Hilton, A.: Computer vision for sports: current applications and research topics. Comput. Vis. Image Underst. 159, 3–18 (2017)
Wang, D.A., Strauss, C.M., Springer, J.M., Thresher, A., Pritchard, H., Kenyon, G.T.: Sparse mp4. In: 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 99–103. IEEE (2020)
Worsey, M.T.O., Espinosa, H.G., Shepherd, J.B., Thiel, D.V.: An evaluation of wearable inertial sensor configuration and supervised machine learning models for automatic punch classification in boxing. IoT 1(2), 360–381 (2020)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Stefański, P., Kozak, J., Jach, T. (2022). The Problem of Detecting Boxers in the Boxing Ring. In: Szczerbicki, E., Wojtkiewicz, K., Nguyen, S.V., Pietranik, M., Krótkiewicz, M. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2022. Communications in Computer and Information Science, vol 1716. Springer, Singapore. https://doi.org/10.1007/978-981-19-8234-7_46
Download citation
DOI: https://doi.org/10.1007/978-981-19-8234-7_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8233-0
Online ISBN: 978-981-19-8234-7
eBook Packages: Computer ScienceComputer Science (R0)