Abstract
In this paper, utilizing a multiscale training dataset, YOLOv8 demonstrates rapid inference capabilities and exceptional accuracy in detecting visual objects, particularly smaller ones. This outperforms transformer-based deep learning models, makes it a leading algorithm in its domain. Typically, the efficacy of visual object detection is gauged by using pre-trained models based on augmented datasets. Yet, for specific situations like table tennis matches and coaching sessions, fine-tuning is essential. Challenges in these scenarios include the rapid ball movement, color, light conditions, and bright reflections caused by intense illumination. In this paper, we introduce a motion-centric algorithm to the YOLOv8 model, aiming to boost the accuracy in predicting ball trajectories, landing spots, and ball velocity within the context of table tennis. Our adapted model not only enhances the real-time applications in sports coaching but also showcases potential for applications in other fast-paced environments. The experimental results indicate an improvement in detection rates and reduced false positives.
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
Xiao, B., Nguyen, M., Yan, W.Q.: Fruit ripeness identification using YOLOv8 model. Multimedia Tools Appl. 1–18 (2023)
Yan, W.: Computational methods for Deep Learning: Theory, Algorithms, and Implementations. Springer, Singapore (2023). https://doi.org/10.1007/978-981-99-4823-9
Tian, B., Zhang, D., Zhang, C.: High-speed tiny tennis ball detection based on deep convolutional neural networks. In International Conference on Anti-counterfeiting, Security, and Identification (ASID), pp. 30–33 (2020)
Cai, G.L.: A method for prediction the trajectory of table tennis in multirotation state based on binocular vision. Comput. Intell. Neurosci. (2022)
Huang, Y., Liao, I., Chen, C., İk, T., Peng, W.: TrackNet: a deep learning network for tracking high-speed and tiny objects in sports applications. In: IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–8 (2019)
Moshayedi, A.J., Chen, Z., Liao, L., Li, S.: Kinect based virtual referee for table tennis game: TTV (Table Tennis Var System). In: International Conference on Information Science and Control Engineering (ICISCE), pp. 354–359 (2019)
Shi, J., Xu, L., Jia, J.: Discriminative blur detection features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2965–2972 (2014)
Wu, Y., Zhang, H., Li, Y., Yang, Y., Yuan, D.: Video object detection guided by object blur evaluation. IEEE Access 8, 208554–208565 (2020)
Zheng, W., Liu, X., Yin, L.: Research on image classification method based on improved multi-scale relational network. J. Comput. Sci. 7, 613 (2012)
Yan, W.: Introduction to Intelligent Surveillance: Surveillance Data Capture, Transmission, and Analytics. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10713-0
Xiao, B., Nguyen, M., Yan, W.Q.: Apple ripeness identification from digital images using transformers. Multimedia Tools Appl. 1–15 (2023)
Liu, Y., Liu, L.: Accurate real-time ball trajectory estimation with onboard stereo camera system for humanoid ping-pong robot. Robot. Auton. Syst. 101, 34–44 (2018)
Mehtab, S., Yan, W.: Flexible neural network for fast and accurate road scene perception. Multimedia Tools Appl. (2022)
Mehtab, S., Yan, W.: FlexiNet: fast and accurate vehicle detection for autonomous vehicles-2D vehicle detection using deep neural network. In: International Conference on Control and Computer Vision (2021)
Xiang, Y., Yan, W.: Fast-moving coin recognition using deep learning. Multimedia Tools Appl. (2021)
Qi, J., Nguyen, M., Yan, W.: Small visual object detection in smart waste classification using transformers with deep learning. In: IVCNZ (2022)
Liu, J., Pan, C., Yan, W.: Litter Detection From Digital Images Using Deep Learning. Springer, Cham (2022). https://doi.org/10.1007/s42979-022-01568-1
Pan, C., Yan, W.: Object detection based on saturation of visual perception. Multimedia Tools Appl. 79(27–28), 19925–19944 (2020)
Pan, C., Yan, W.: A learning-based positive feedback in salient object detection. In: IVCNZ (2018)
Pan, C., Liu, J., Yan, W., Zhou, Y.: Salient object detection based on visual perceptual saturation and two-stream hybrid networks. IEEE Trans. Image Process. (2021)
Qi, J., Nguyen, M., Yan, W.: CISO: co-iteration semi-supervised learning for visual object detection. Multimedia Tools Appl. (2023)
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 Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhou, H., Nguyen, M., Yan, W.Q. (2024). Computational Analysis of Table Tennis Matches from Real-Time Videos Using Deep Learning. In: Yan, W.Q., Nguyen, M., Nand, P., Li, X. (eds) Image and Video Technology. PSIVT 2023. Lecture Notes in Computer Science, vol 14403. Springer, Singapore. https://doi.org/10.1007/978-981-97-0376-0_6
Download citation
DOI: https://doi.org/10.1007/978-981-97-0376-0_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0375-3
Online ISBN: 978-981-97-0376-0
eBook Packages: Computer ScienceComputer Science (R0)