Abstract
This chapter introduces convolutional neural networks (CNNs) and describes how they can be used in the context of sports analytics. CNNs are suitable for end-to-end learning on images or similarly structured data. CNNs can efficiently learn features of images based on pixel values and, for example, extract suitable features for a classification task. In this context, the models benefit from parameter sharing in the convolutional layers and exhibit translation equivariance and invariance properties. CNNs are thus suited for learning features from positional data of team sports, provided that the data is put into an appropriate structure.
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
References
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In IEEE conference on computer vision and pattern recognition (pp. 248–255).
Dick, U., & Brefeld, U. (2019). Learning to rate player positioning in soccer. Big Data, 7, 71–82.
Fassmeyer, D., Anzer, G., Bauer, P., & Brefeld, U. (2021). Toward automatically labeling situations in soccer. Frontiers in Sports and Active Living, 3, 725431.
Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36, 193–120.
Gerke, S., Müller, K. & Schäfer, R. (2015). Soccer Jersey Number Recognition Using Convolutional Neural Networks. IEEE International Conference on Computer Vision Workshop.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In IEEE conference on computer vision and pattern recognition (pp. 770–778).
Johnson, S., & Everingham, M. (2011). Clustered pose and nonlinear appearance models for human pose estimation. In IEEE conference on computer vision and pattern recognition.
Kayhan, O. S., & van Gemert, J. C. (2020). On translation invariance in CNNs: Convolutional layers can exploit absolute spatial location. In IEEE conference on computer vision and pattern recognition.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021). Zero-shot text-to-image generation. In International conference on machine learning.
Rudolph, Y., & Brefeld, U. (2022). Modeling conditional dependencies in multiagent trajectories. In International conference on artificial intelligence and statistics.
Wei, S.-E., Ramakrishna, V., Kanade, T., & Sheikh, Y. (2016). Convolutional pose machines. In IEEE conference on computer vision and pattern recognition.
Yeh, R. A., Schwing, A. G., Huang, J., & Murphy, K. (2019). Diverse generation for multi-agent sports games. In IEEE conference on computer vision and pattern recognition.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature
About this chapter
Cite this chapter
Rudolph, Y., Brefeld, U. (2024). Convolutional Neural Networks. In: Memmert, D. (eds) Computer Science in Sport. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-68313-2_22
Download citation
DOI: https://doi.org/10.1007/978-3-662-68313-2_22
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-68312-5
Online ISBN: 978-3-662-68313-2
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)